► 代码示例 / 结构化数据 / 基于 Transformer 的推荐系统

基于 Transformer 的推荐系统

作者： Khalid Salama
创建日期 2020/12/30
最后修改 2025/01/27
描述： 使用 Movielens 数据集上的行为序列 Transformer (BST) 模型进行评分预测。

ⓘ 本示例使用 Keras 3

在 Colab 中查看 • GitHub 源代码

引言

本示例使用 Movielens 数据集演示了由 Qiwei Chen 等人提出的行为序列 Transformer (BST) 模型。BST 模型利用用户观看和评分电影的顺序行为以及用户资料和电影特征来预测用户对目标电影的评分。

更具体地说，BST 模型旨在通过接受以下输入来预测目标电影的评分：

用户观看的固定长度的 movie_ids 序列。
用户观看的电影的固定长度的 ratings 序列。
一组用户特征，包括 user_id、sex、occupation 和 age_group。
输入序列中的每部电影和目标电影的一组 genres。
用于预测评分的 target_movie_id。

本示例对原始 BST 模型进行了以下修改：

我们将电影特征（genres）整合到输入序列中每部电影和目标电影的嵌入向量处理中，而不是将它们视为 transformer 层之外的“其他特征”。
我们利用输入序列中电影的评分及其在序列中的位置来更新它们，然后再将它们馈送到自注意力层。

请注意，本示例应使用 TensorFlow 2.4 或更高版本运行。

数据集

我们使用了 Movielens 数据集的 1M 版本。该数据集包含约 100 万条评分，来自 6000 名用户对 4000 部电影的评分，以及一些用户特征、电影类型。此外，每条用户-电影评分都提供了时间戳，这使得可以为每个用户创建电影评分序列，正如 BST 模型所期望的那样。

设置

import os

os.environ["KERAS_BACKEND"] = "jax"  # or torch, or tensorflow

import math
from zipfile import ZipFile
from urllib.request import urlretrieve
import numpy as np
import pandas as pd

import keras
from keras import layers, ops
from keras.layers import StringLookup

准备数据

下载并准备 DataFrames

首先，我们下载 movielens 数据。

下载的文件夹将包含三个数据文件：users.dat、movies.dat 和 ratings.dat。

urlretrieve("http://files.grouplens.org/datasets/movielens/ml-1m.zip", "movielens.zip")
ZipFile("movielens.zip", "r").extractall()

然后，我们将数据加载到具有适当列名的 pandas DataFrames 中。

users = pd.read_csv(
    "ml-1m/users.dat",
    sep="::",
    names=["user_id", "sex", "age_group", "occupation", "zip_code"],
    encoding="ISO-8859-1",
    engine="python",
)

ratings = pd.read_csv(
    "ml-1m/ratings.dat",
    sep="::",
    names=["user_id", "movie_id", "rating", "unix_timestamp"],
    encoding="ISO-8859-1",
    engine="python",
)

movies = pd.read_csv(
    "ml-1m/movies.dat",
    sep="::",
    names=["movie_id", "title", "genres"],
    encoding="ISO-8859-1",
    engine="python",
)

在这里，我们进行一些简单的数据处理以修复列的数据类型。

users["user_id"] = users["user_id"].apply(lambda x: f"user_{x}")
users["age_group"] = users["age_group"].apply(lambda x: f"group_{x}")
users["occupation"] = users["occupation"].apply(lambda x: f"occupation_{x}")

movies["movie_id"] = movies["movie_id"].apply(lambda x: f"movie_{x}")

ratings["movie_id"] = ratings["movie_id"].apply(lambda x: f"movie_{x}")
ratings["user_id"] = ratings["user_id"].apply(lambda x: f"user_{x}")
ratings["rating"] = ratings["rating"].apply(lambda x: float(x))

每部电影都有多种类型。我们在 movies DataFrame 中将它们拆分为单独的列。

genres = ["Action", "Adventure", "Animation", "Children's", "Comedy", "Crime"]
genres += ["Documentary", "Drama", "Fantasy", "Film-Noir", "Horror", "Musical"]
genres += ["Mystery", "Romance", "Sci-Fi", "Thriller", "War", "Western"]

for genre in genres:
    movies[genre] = movies["genres"].apply(
        lambda values: int(genre in values.split("|"))
    )

将电影评分数据转换为序列

首先，我们使用 unix_timestamp 对评分数据进行排序，然后按 user_id 对 movie_id 值和 rating 值进行分组。

输出的 DataFrame 将为每个 user_id 生成一条记录，包含两个有序列表（按评分日期时间排序）：他们评分过的电影，以及他们对这些电影的评分。

ratings_group = ratings.sort_values(by=["unix_timestamp"]).groupby("user_id")

ratings_data = pd.DataFrame(
    data={
        "user_id": list(ratings_group.groups.keys()),
        "movie_ids": list(ratings_group.movie_id.apply(list)),
        "ratings": list(ratings_group.rating.apply(list)),
        "timestamps": list(ratings_group.unix_timestamp.apply(list)),
    }
)

现在，我们将 movie_ids 列表分割成一组固定长度的序列。对 ratings 也进行同样的处理。设置 sequence_length 变量来改变模型输入序列的长度。你还可以改变 step_size 来控制为每个用户生成的序列数量。

sequence_length = 4
step_size = 2


def create_sequences(values, window_size, step_size):
    sequences = []
    start_index = 0
    while True:
        end_index = start_index + window_size
        seq = values[start_index:end_index]
        if len(seq) < window_size:
            seq = values[-window_size:]
            if len(seq) == window_size:
                sequences.append(seq)
            break
        sequences.append(seq)
        start_index += step_size
    return sequences


ratings_data.movie_ids = ratings_data.movie_ids.apply(
    lambda ids: create_sequences(ids, sequence_length, step_size)
)

ratings_data.ratings = ratings_data.ratings.apply(
    lambda ids: create_sequences(ids, sequence_length, step_size)
)

del ratings_data["timestamps"]

之后，我们处理输出，使每个序列在 DataFrame 中成为单独的记录。此外，我们将用户特征与评分数据连接起来。

ratings_data_movies = ratings_data[["user_id", "movie_ids"]].explode(
    "movie_ids", ignore_index=True
)
ratings_data_rating = ratings_data[["ratings"]].explode("ratings", ignore_index=True)
ratings_data_transformed = pd.concat([ratings_data_movies, ratings_data_rating], axis=1)
ratings_data_transformed = ratings_data_transformed.join(
    users.set_index("user_id"), on="user_id"
)
ratings_data_transformed.movie_ids = ratings_data_transformed.movie_ids.apply(
    lambda x: ",".join(x)
)
ratings_data_transformed.ratings = ratings_data_transformed.ratings.apply(
    lambda x: ",".join([str(v) for v in x])
)

del ratings_data_transformed["zip_code"]

ratings_data_transformed.rename(
    columns={"movie_ids": "sequence_movie_ids", "ratings": "sequence_ratings"},
    inplace=True,
)

将 sequence_length 设置为 4，step_size 设置为 2 时，我们最终得到 498,623 个序列。

最后，我们将数据分别按 85% 和 15% 的比例分割为训练集和测试集，并将它们存储到 CSV 文件中。

random_selection = np.random.rand(len(ratings_data_transformed.index)) <= 0.85
train_data = ratings_data_transformed[random_selection]
test_data = ratings_data_transformed[~random_selection]

train_data.to_csv("train_data.csv", index=False, sep="|", header=False)
test_data.to_csv("test_data.csv", index=False, sep="|", header=False)

定义元数据

CSV_HEADER = list(ratings_data_transformed.columns)

CATEGORICAL_FEATURES_WITH_VOCABULARY = {
    "user_id": list(users.user_id.unique()),
    "movie_id": list(movies.movie_id.unique()),
    "sex": list(users.sex.unique()),
    "age_group": list(users.age_group.unique()),
    "occupation": list(users.occupation.unique()),
}

USER_FEATURES = ["sex", "age_group", "occupation"]

MOVIE_FEATURES = ["genres"]

编码输入特征

encode_input_features 函数的工作方式如下：

每个分类用户特征使用 layers.Embedding 进行编码，嵌入维度等于该特征词汇量大小的平方根。这些特征的嵌入向量被拼接起来形成一个单一的输入张量。
电影序列中的每部电影和目标电影都使用 layers.Embedding 进行编码，维度大小是电影数量的平方根。
每部电影的多热 genres 向量与其嵌入向量拼接起来，并使用非线性 layers.Dense 进行处理，输出一个与电影嵌入维度相同的向量。
位置嵌入被添加到序列中的每个电影嵌入向量中，然后乘以其在评分序列中的评分。
目标电影嵌入向量与序列电影嵌入向量拼接，产生一个形状为 [batch size, sequence length, embedding size] 的张量，正如 transformer 架构的注意力层所期望的那样。
该方法返回一个包含两个元素的元组：encoded_transformer_features 和 encoded_other_features。

# Required for tf.data.Dataset
import tensorflow as tf


def get_dataset_from_csv(csv_file_path, batch_size, shuffle=True):

    def process(features):
        movie_ids_string = features["sequence_movie_ids"]
        sequence_movie_ids = tf.strings.split(movie_ids_string, ",").to_tensor()
        # The last movie id in the sequence is the target movie.
        features["target_movie_id"] = sequence_movie_ids[:, -1]
        features["sequence_movie_ids"] = sequence_movie_ids[:, :-1]
        # Sequence ratings
        ratings_string = features["sequence_ratings"]
        sequence_ratings = tf.strings.to_number(
            tf.strings.split(ratings_string, ","), tf.dtypes.float32
        ).to_tensor()
        # The last rating in the sequence is the target for the model to predict.
        target = sequence_ratings[:, -1]
        features["sequence_ratings"] = sequence_ratings[:, :-1]

        def encoding_helper(feature_name):

            # This are target_movie_id and sequence_movie_ids and they have the same
            # vocabulary as movie_id.
            if feature_name not in CATEGORICAL_FEATURES_WITH_VOCABULARY:
                vocabulary = CATEGORICAL_FEATURES_WITH_VOCABULARY["movie_id"]
                index_lookup = StringLookup(
                    vocabulary=vocabulary, mask_token=None, num_oov_indices=0
                )
                # Convert the string input values into integer indices.
                value_index = index_lookup(features[feature_name])
                features[feature_name] = value_index
            else:
                # movie_id is not part of the features, hence not processed. It was mainly required
                # for its vocabulary above.
                if feature_name == "movie_id":
                    pass
                else:
                    vocabulary = CATEGORICAL_FEATURES_WITH_VOCABULARY[feature_name]
                    index_lookup = StringLookup(
                        vocabulary=vocabulary, mask_token=None, num_oov_indices=0
                    )
                    # Convert the string input values into integer indices.
                    value_index = index_lookup(features[feature_name])
                    features[feature_name] = value_index

        # Encode the user features
        for feature_name in CATEGORICAL_FEATURES_WITH_VOCABULARY:
            encoding_helper(feature_name)
        # Encoding target_movie_id and returning it as the target variable
        encoding_helper("target_movie_id")
        # Encoding sequence movie_ids.
        encoding_helper("sequence_movie_ids")
        return dict(features), target

    dataset = tf.data.experimental.make_csv_dataset(
        csv_file_path,
        batch_size=batch_size,
        column_names=CSV_HEADER,
        num_epochs=1,
        header=False,
        field_delim="|",
        shuffle=shuffle,
    ).map(process)
    return dataset


def encode_input_features(
    inputs,
    include_user_id,
    include_user_features,
    include_movie_features,
):
    encoded_transformer_features = []
    encoded_other_features = []

    other_feature_names = []
    if include_user_id:
        other_feature_names.append("user_id")
    if include_user_features:
        other_feature_names.extend(USER_FEATURES)

    ## Encode user features
    for feature_name in other_feature_names:
        vocabulary = CATEGORICAL_FEATURES_WITH_VOCABULARY[feature_name]
        # Compute embedding dimensions
        embedding_dims = int(math.sqrt(len(vocabulary)))
        # Create an embedding layer with the specified dimensions.
        embedding_encoder = layers.Embedding(
            input_dim=len(vocabulary),
            output_dim=embedding_dims,
            name=f"{feature_name}_embedding",
        )
        # Convert the index values to embedding representations.
        encoded_other_features.append(embedding_encoder(inputs[feature_name]))

    ## Create a single embedding vector for the user features
    if len(encoded_other_features) > 1:
        encoded_other_features = layers.concatenate(encoded_other_features)
    elif len(encoded_other_features) == 1:
        encoded_other_features = encoded_other_features[0]
    else:
        encoded_other_features = None

    ## Create a movie embedding encoder
    movie_vocabulary = CATEGORICAL_FEATURES_WITH_VOCABULARY["movie_id"]
    movie_embedding_dims = int(math.sqrt(len(movie_vocabulary)))
    # Create an embedding layer with the specified dimensions.
    movie_embedding_encoder = layers.Embedding(
        input_dim=len(movie_vocabulary),
        output_dim=movie_embedding_dims,
        name=f"movie_embedding",
    )
    # Create a vector lookup for movie genres.
    genre_vectors = movies[genres].to_numpy()
    movie_genres_lookup = layers.Embedding(
        input_dim=genre_vectors.shape[0],
        output_dim=genre_vectors.shape[1],
        embeddings_initializer=keras.initializers.Constant(genre_vectors),
        trainable=False,
        name="genres_vector",
    )
    # Create a processing layer for genres.
    movie_embedding_processor = layers.Dense(
        units=movie_embedding_dims,
        activation="relu",
        name="process_movie_embedding_with_genres",
    )

    ## Define a function to encode a given movie id.
    def encode_movie(movie_id):
        # Convert the string input values into integer indices.
        movie_embedding = movie_embedding_encoder(movie_id)
        encoded_movie = movie_embedding
        if include_movie_features:
            movie_genres_vector = movie_genres_lookup(movie_id)
            encoded_movie = movie_embedding_processor(
                layers.concatenate([movie_embedding, movie_genres_vector])
            )
        return encoded_movie

    ## Encoding target_movie_id
    target_movie_id = inputs["target_movie_id"]
    encoded_target_movie = encode_movie(target_movie_id)

    ## Encoding sequence movie_ids.
    sequence_movies_ids = inputs["sequence_movie_ids"]
    encoded_sequence_movies = encode_movie(sequence_movies_ids)
    # Create positional embedding.
    position_embedding_encoder = layers.Embedding(
        input_dim=sequence_length,
        output_dim=movie_embedding_dims,
        name="position_embedding",
    )
    positions = ops.arange(start=0, stop=sequence_length - 1, step=1)
    encodded_positions = position_embedding_encoder(positions)
    # Retrieve sequence ratings to incorporate them into the encoding of the movie.
    sequence_ratings = inputs["sequence_ratings"]
    sequence_ratings = ops.expand_dims(sequence_ratings, -1)
    # Add the positional encoding to the movie encodings and multiply them by rating.
    encoded_sequence_movies_with_poistion_and_rating = layers.Multiply()(
        [(encoded_sequence_movies + encodded_positions), sequence_ratings]
    )

    # Construct the transformer inputs.
    for i in range(sequence_length - 1):
        feature = encoded_sequence_movies_with_poistion_and_rating[:, i, ...]
        feature = ops.expand_dims(feature, 1)
        encoded_transformer_features.append(feature)
    encoded_transformer_features.append(encoded_target_movie)
    encoded_transformer_features = layers.concatenate(
        encoded_transformer_features, axis=1
    )
    return encoded_transformer_features, encoded_other_features

创建模型输入

def create_model_inputs():
    return {
        "user_id": keras.Input(name="user_id", shape=(1,), dtype="int32"),
        "sequence_movie_ids": keras.Input(
            name="sequence_movie_ids", shape=(sequence_length - 1,), dtype="int32"
        ),
        "target_movie_id": keras.Input(
            name="target_movie_id", shape=(1,), dtype="int32"
        ),
        "sequence_ratings": keras.Input(
            name="sequence_ratings", shape=(sequence_length - 1,), dtype="float32"
        ),
        "sex": keras.Input(name="sex", shape=(1,), dtype="int32"),
        "age_group": keras.Input(name="age_group", shape=(1,), dtype="int32"),
        "occupation": keras.Input(name="occupation", shape=(1,), dtype="int32"),
    }

创建一个 BST 模型

include_user_id = False
include_user_features = False
include_movie_features = False

hidden_units = [256, 128]
dropout_rate = 0.1
num_heads = 3


def create_model():

    inputs = create_model_inputs()
    transformer_features, other_features = encode_input_features(
        inputs, include_user_id, include_user_features, include_movie_features
    )
    # Create a multi-headed attention layer.
    attention_output = layers.MultiHeadAttention(
        num_heads=num_heads, key_dim=transformer_features.shape[2], dropout=dropout_rate
    )(transformer_features, transformer_features)

    # Transformer block.
    attention_output = layers.Dropout(dropout_rate)(attention_output)
    x1 = layers.Add()([transformer_features, attention_output])
    x1 = layers.LayerNormalization()(x1)
    x2 = layers.LeakyReLU()(x1)
    x2 = layers.Dense(units=x2.shape[-1])(x2)
    x2 = layers.Dropout(dropout_rate)(x2)
    transformer_features = layers.Add()([x1, x2])
    transformer_features = layers.LayerNormalization()(transformer_features)
    features = layers.Flatten()(transformer_features)

    # Included the other_features.
    if other_features is not None:
        features = layers.concatenate(
            [features, layers.Reshape([other_features.shape[-1]])(other_features)]
        )

    # Fully-connected layers.
    for num_units in hidden_units:
        features = layers.Dense(num_units)(features)
        features = layers.BatchNormalization()(features)
        features = layers.LeakyReLU()(features)
        features = layers.Dropout(dropout_rate)(features)
    outputs = layers.Dense(units=1)(features)
    model = keras.Model(inputs=inputs, outputs=outputs)
    return model


model = create_model()

An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.

运行训练和评估实验

# Compile the model.
model.compile(
    optimizer=keras.optimizers.Adagrad(learning_rate=0.01),
    loss=keras.losses.MeanSquaredError(),
    metrics=[keras.metrics.MeanAbsoluteError()],
)

# Read the training data.

train_dataset = get_dataset_from_csv("train_data.csv", batch_size=265, shuffle=True)

# Fit the model with the training data.
model.fit(train_dataset, epochs=2)

# Read the test data.
test_dataset = get_dataset_from_csv("test_data.csv", batch_size=265)

# Evaluate the model on the test data.
_, rmse = model.evaluate(test_dataset, verbose=0)
print(f"Test MAE: {round(rmse, 3)}")

Epoch 1/2

  1/Unknown  7s 7s/step - loss: 12.6790 - mean_absolute_error: 3.2470


  2/Unknown  7s 175ms/step - loss: 11.8442 - mean_absolute_error: 3.1216


  3/Unknown  7s 169ms/step - loss: 11.2701 - mean_absolute_error: 3.0387


  4/Unknown  7s 162ms/step - loss: 10.7890 - mean_absolute_error: 2.9640


  5/Unknown  8s 160ms/step - loss: 10.3522 - mean_absolute_error: 2.8934


  6/Unknown  8s 173ms/step - loss: 9.9734 - mean_absolute_error: 2.8301 


  7/Unknown  8s 181ms/step - loss: 9.6343 - mean_absolute_error: 2.7711


  8/Unknown  8s 176ms/step - loss: 9.3284 - mean_absolute_error: 2.7167


  9/Unknown  8s 174ms/step - loss: 9.0492 - mean_absolute_error: 2.6662


 10/Unknown  8s 172ms/step - loss: 8.7926 - mean_absolute_error: 2.6188


 11/Unknown  9s 171ms/step - loss: 8.5587 - mean_absolute_error: 2.5750


 12/Unknown  9s 169ms/step - loss: 8.3417 - mean_absolute_error: 2.5337


 13/Unknown  9s 169ms/step - loss: 8.1379 - mean_absolute_error: 2.4944


 14/Unknown  9s 169ms/step - loss: 7.9466 - mean_absolute_error: 2.4568


 15/Unknown  9s 168ms/step - loss: 7.7681 - mean_absolute_error: 2.4216


 16/Unknown  9s 168ms/step - loss: 7.6015 - mean_absolute_error: 2.3885


 17/Unknown  10s 168ms/step - loss: 7.4451 - mean_absolute_error: 2.3571


 18/Unknown  10s 168ms/step - loss: 7.2978 - mean_absolute_error: 2.3273


 19/Unknown  10s 167ms/step - loss: 7.1582 - mean_absolute_error: 2.2988


 20/Unknown  10s 167ms/step - loss: 7.0262 - mean_absolute_error: 2.2716


 21/Unknown  10s 167ms/step - loss: 6.9011 - mean_absolute_error: 2.2458


 22/Unknown  10s 166ms/step - loss: 6.7823 - mean_absolute_error: 2.2211


 23/Unknown  11s 166ms/step - loss: 6.6689 - mean_absolute_error: 2.1972


 24/Unknown  11s 167ms/step - loss: 6.5609 - mean_absolute_error: 2.1744


 25/Unknown  11s 166ms/step - loss: 6.4579 - mean_absolute_error: 2.1524


 26/Unknown  11s 166ms/step - loss: 6.3597 - mean_absolute_error: 2.1315


 27/Unknown  11s 166ms/step - loss: 6.2656 - mean_absolute_error: 2.1112


 28/Unknown  11s 166ms/step - loss: 6.1754 - mean_absolute_error: 2.0917


 29/Unknown  12s 166ms/step - loss: 6.0891 - mean_absolute_error: 2.0730


 30/Unknown  12s 166ms/step - loss: 6.0059 - mean_absolute_error: 2.0548


 31/Unknown  12s 166ms/step - loss: 5.9260 - mean_absolute_error: 2.0372


 32/Unknown  12s 166ms/step - loss: 5.8491 - mean_absolute_error: 2.0203


 33/Unknown  12s 167ms/step - loss: 5.7751 - mean_absolute_error: 2.0039


 34/Unknown  12s 167ms/step - loss: 5.7038 - mean_absolute_error: 1.9880


 35/Unknown  13s 167ms/step - loss: 5.6349 - mean_absolute_error: 1.9727


 36/Unknown  13s 168ms/step - loss: 5.5687 - mean_absolute_error: 1.9579


 37/Unknown  13s 168ms/step - loss: 5.5050 - mean_absolute_error: 1.9435


 38/Unknown  13s 169ms/step - loss: 5.4435 - mean_absolute_error: 1.9297


 39/Unknown  13s 170ms/step - loss: 5.3839 - mean_absolute_error: 1.9163


 40/Unknown  13s 170ms/step - loss: 5.3267 - mean_absolute_error: 1.9033


 41/Unknown  14s 170ms/step - loss: 5.2712 - mean_absolute_error: 1.8908


 42/Unknown  14s 171ms/step - loss: 5.2175 - mean_absolute_error: 1.8786


 43/Unknown  14s 171ms/step - loss: 5.1653 - mean_absolute_error: 1.8667


 44/Unknown  14s 171ms/step - loss: 5.1147 - mean_absolute_error: 1.8551


 45/Unknown  14s 170ms/step - loss: 5.0658 - mean_absolute_error: 1.8439


 46/Unknown  15s 170ms/step - loss: 5.0183 - mean_absolute_error: 1.8330


 47/Unknown  15s 171ms/step - loss: 4.9722 - mean_absolute_error: 1.8224


 48/Unknown  15s 171ms/step - loss: 4.9275 - mean_absolute_error: 1.8121


 49/Unknown  15s 171ms/step - loss: 4.8840 - mean_absolute_error: 1.8021


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1599/1599 ━━━━━━━━━━━━━━━━━━━━ 364s 224ms/step - loss: 1.5254 - mean_absolute_error: 0.9677

Epoch 2/2

/home/humbulani/tensorflow-env/env/lib/python3.11/site-packages/keras/src/trainers/epoch_iterator.py:159: UserWarning: Your input ran out of data; interrupting training. Make sure that your dataset or generator can generate at least `steps_per_epoch * epochs` batches. You may need to use the `.repeat()` function when building your dataset.
  self._interrupted_warning()

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27/1599 [37m━━━━━━━━━━━━━━━━━━━━ 6:19 242ms/step - loss: 1.0833 - mean_absolute_error: 0.8286

28/1599 [37m━━━━━━━━━━━━━━━━━━━━ 6:17 241ms/step - loss: 1.0828 - mean_absolute_error: 0.8286

29/1599 [37m━━━━━━━━━━━━━━━━━━━━ 6:15 239ms/step - loss: 1.0822 - mean_absolute_error: 0.8284

30/1599 [37m━━━━━━━━━━━━━━━━━━━━ 6:14 239ms/step - loss: 1.0817 - mean_absolute_error: 0.8284

31/1599 [37m━━━━━━━━━━━━━━━━━━━━ 6:12 238ms/step - loss: 1.0811 - mean_absolute_error: 0.8283

32/1599 [37m━━━━━━━━━━━━━━━━━━━━ 6:11 237ms/step - loss: 1.0805 - mean_absolute_error: 0.8281

33/1599 [37m━━━━━━━━━━━━━━━━━━━━ 6:11 237ms/step - loss: 1.0799 - mean_absolute_error: 0.8280

34/1599 [37m━━━━━━━━━━━━━━━━━━━━ 6:10 237ms/step - loss: 1.0792 - mean_absolute_error: 0.8279

35/1599 [37m━━━━━━━━━━━━━━━━━━━━ 6:09 236ms/step - loss: 1.0787 - mean_absolute_error: 0.8278

36/1599 [37m━━━━━━━━━━━━━━━━━━━━ 6:08 236ms/step - loss: 1.0781 - mean_absolute_error: 0.8277

37/1599 [37m━━━━━━━━━━━━━━━━━━━━ 6:07 236ms/step - loss: 1.0776 - mean_absolute_error: 0.8276

38/1599 [37m━━━━━━━━━━━━━━━━━━━━ 6:07 235ms/step - loss: 1.0771 - mean_absolute_error: 0.8275

39/1599 [37m━━━━━━━━━━━━━━━━━━━━ 6:06 235ms/step - loss: 1.0766 - mean_absolute_error: 0.8274

40/1599 [37m━━━━━━━━━━━━━━━━━━━━ 6:06 235ms/step - loss: 1.0761 - mean_absolute_error: 0.8273

41/1599 [37m━━━━━━━━━━━━━━━━━━━━ 6:05 234ms/step - loss: 1.0757 - mean_absolute_error: 0.8272

42/1599 [37m━━━━━━━━━━━━━━━━━━━━ 6:04 234ms/step - loss: 1.0753 - mean_absolute_error: 0.8272

43/1599 [37m━━━━━━━━━━━━━━━━━━━━ 6:04 234ms/step - loss: 1.0748 - mean_absolute_error: 0.8270

44/1599 [37m━━━━━━━━━━━━━━━━━━━━ 6:03 234ms/step - loss: 1.0743 - mean_absolute_error: 0.8269

45/1599 [37m━━━━━━━━━━━━━━━━━━━━ 6:04 234ms/step - loss: 1.0738 - mean_absolute_error: 0.8268

46/1599 [37m━━━━━━━━━━━━━━━━━━━━ 6:05 235ms/step - loss: 1.0734 - mean_absolute_error: 0.8267

47/1599 [37m━━━━━━━━━━━━━━━━━━━━ 6:06 236ms/step - loss: 1.0730 - mean_absolute_error: 0.8266

48/1599 [37m━━━━━━━━━━━━━━━━━━━━ 6:07 237ms/步 - 损失: 1.0726 - 平均绝对误差: 0.8265

49/1599 [37m━━━━━━━━━━━━━━━━━━━━ 6:06 236ms/步 - 损失: 1.0724 - 平均绝对误差: 0.8265

50/1599 [37m━━━━━━━━━━━━━━━━━━━━ 6:06 237ms/步 - 损失: 1.0722 - 平均绝对误差: 0.8265

51/1599 [37m━━━━━━━━━━━━━━━━━━━━ 6:21 247ms/步 - 损失: 1.0721 - 平均绝对误差: 0.8265

52/1599 [37m━━━━━━━━━━━━━━━━━━━━ 6:21 246ms/步 - 损失: 1.0720 - 平均绝对误差: 0.8265

53/1599 [37m━━━━━━━━━━━━━━━━━━━━ 6:19 246ms/步 - 损失: 1.0719 - 平均绝对误差: 0.8265

54/1599 [37m━━━━━━━━━━━━━━━━━━━━ 6:19 245ms/步 - 损失: 1.0717 - 平均绝对误差: 0.8265

55/1599 [37m━━━━━━━━━━━━━━━━━━━━ 6:17 245ms/步 - 损失: 1.0716 - 平均绝对误差: 0.8265

56/1599 [37m━━━━━━━━━━━━━━━━━━━━ 6:16 244ms/步 - 损失: 1.0714 - 平均绝对误差: 0.8265

57/1599 [37m━━━━━━━━━━━━━━━━━━━━ 6:15 243ms/步 - 损失: 1.0712 - 平均绝对误差: 0.8265

58/1599 [37m━━━━━━━━━━━━━━━━━━━━ 6:13 243ms/步 - 损失: 1.0709 - 平均绝对误差: 0.8265

59/1599 [37m━━━━━━━━━━━━━━━━━━━━ 6:12 242ms/步 - 损失: 1.0707 - 平均绝对误差: 0.8264

60/1599 [37m━━━━━━━━━━━━━━━━━━━━ 6:11 242ms/步 - 损失: 1.0704 - 平均绝对误差: 0.8264

61/1599 [37m━━━━━━━━━━━━━━━━━━━━ 6:11 241ms/步 - 损失: 1.0701 - 平均绝对误差: 0.8263

62/1599 [37m━━━━━━━━━━━━━━━━━━━━ 6:10 241ms/步 - 损失: 1.0699 - 平均绝对误差: 0.8263

63/1599 [37m━━━━━━━━━━━━━━━━━━━━ 6:09 241ms/步 - 损失: 1.0696 - 平均绝对误差: 0.8262

64/1599 [37m━━━━━━━━━━━━━━━━━━━━ 6:09 240ms/步 - 损失: 1.0694 - 平均绝对误差: 0.8262

65/1599 [37m━━━━━━━━━━━━━━━━━━━━ 6:08 240ms/步 - 损失: 1.0691 - 平均绝对误差: 0.8261

66/1599 [37m━━━━━━━━━━━━━━━━━━━━ 6:07 240ms/步 - 损失: 1.0690 - 平均绝对误差: 0.8261

67/1599 [37m━━━━━━━━━━━━━━━━━━━━ 6:07 240ms/步 - 损失: 1.0688 - 平均绝对误差: 0.8261

68/1599 [37m━━━━━━━━━━━━━━━━━━━━ 6:06 239ms/步 - 损失: 1.0686 - 平均绝对误差: 0.8261

69/1599 [37m━━━━━━━━━━━━━━━━━━━━ 6:05 239ms/步 - 损失: 1.0684 - 平均绝对误差: 0.8260

70/1599 [37m━━━━━━━━━━━━━━━━━━━━ 6:05 239ms/步 - 损失: 1.0682 - 平均绝对误差: 0.8260

71/1599 [37m━━━━━━━━━━━━━━━━━━━━ 6:04 239ms/步 - 损失: 1.0680 - 平均绝对误差: 0.8260

72/1599 [37m━━━━━━━━━━━━━━━━━━━━ 6:04 239ms/步 - 损失: 1.0678 - 平均绝对误差: 0.8259

73/1599 [37m━━━━━━━━━━━━━━━━━━━━ 6:03 238ms/步 - 损失: 1.0676 - 平均绝对误差: 0.8259

74/1599 [37m━━━━━━━━━━━━━━━━━━━━ 6:03 239ms/步 - 损失: 1.0674 - 平均绝对误差: 0.8259

75/1599 [37m━━━━━━━━━━━━━━━━━━━━ 6:04 239ms/步 - 损失: 1.0672 - 平均绝对误差: 0.8258

76/1599 [37m━━━━━━━━━━━━━━━━━━━━ 6:04 240ms/步 - 损失: 1.0670 - 平均绝对误差: 0.8258

77/1599 [37m━━━━━━━━━━━━━━━━━━━━ 6:05 240ms/步 - 损失: 1.0668 - 平均绝对误差: 0.8258

78/1599 [37m━━━━━━━━━━━━━━━━━━━━ 6:05 240ms/步 - 损失: 1.0666 - 平均绝对误差: 0.8257

79/1599 [37m━━━━━━━━━━━━━━━━━━━━ 6:05 241ms/步 - 损失: 1.0664 - 平均绝对误差: 0.8257

80/1599 ━ [37m━━━━━━━━━━━━━━━━━━━ 6:06 241ms/步 - 损失: 1.0662 - 平均绝对误差: 0.8257

81/1599 ━ [37m━━━━━━━━━━━━━━━━━━━ 6:06 241ms/步 - 损失: 1.0661 - 平均绝对误差: 0.8257

82/1599 ━ [37m━━━━━━━━━━━━━━━━━━━ 6:05 241ms/步 - 损失: 1.0659 - 平均绝对误差: 0.8256

83/1599 ━ [37m━━━━━━━━━━━━━━━━━━━ 6:05 241ms/步 - 损失: 1.0658 - 平均绝对误差: 0.8256

84/1599 ━ [37m━━━━━━━━━━━━━━━━━━━ 6:05 241ms/步 - 损失: 1.0657 - 平均绝对误差: 0.8256

85/1599 ━ [37m━━━━━━━━━━━━━━━━━━━ 6:05 241ms/步 - 损失: 1.0656 - 平均绝对误差: 0.8256

86/1599 ━ [37m━━━━━━━━━━━━━━━━━━━ 6:04 241ms/步 - 损失: 1.0655 - 平均绝对误差: 0.8256

87/1599 ━ [37m━━━━━━━━━━━━━━━━━━━ 6:04 241ms/步 - 损失: 1.0653 - 平均绝对误差: 0.8256

88/1599 ━ [37m━━━━━━━━━━━━━━━━━━━ 6:03 241ms/步 - 损失: 1.0652 - 平均绝对误差: 0.8256

89/1599 ━ [37m━━━━━━━━━━━━━━━━━━━ 6:03 241ms/步 - 损失: 1.0651 - 平均绝对误差: 0.8256

90/1599 ━ [37m━━━━━━━━━━━━━━━━━━━ 6:03 241ms/步 - 损失: 1.0651 - 平均绝对误差: 0.8256

91/1599 ━ [37m━━━━━━━━━━━━━━━━━━━ 6:03 241ms/步 - 损失: 1.0650 - 平均绝对误差: 0.8256

92/1599 ━ [37m━━━━━━━━━━━━━━━━━━━ 6:03 241ms/步 - 损失: 1.0649 - 平均绝对误差: 0.8256

93/1599 ━ [37m━━━━━━━━━━━━━━━━━━━ 6:02 241ms/步 - 损失: 1.0649 - 平均绝对误差: 0.8256

94/1599 ━ [37m━━━━━━━━━━━━━━━━━━━ 6:02 241ms/步 - 损失: 1.0648 - 平均绝对误差: 0.8256

95/1599 ━ [37m━━━━━━━━━━━━━━━━━━━ 6:01 241ms/步 - 损失: 1.0648 - 平均绝对误差: 0.8256

96/1599 ━ [37m━━━━━━━━━━━━━━━━━━━ 6:02 241ms/步 - 损失: 1.0647 - 平均绝对误差: 0.8256

97/1599 ━ [37m━━━━━━━━━━━━━━━━━━━ 6:02 241ms/步 - 损失: 1.0647 - 平均绝对误差: 0.8256

98/1599 ━ [37m━━━━━━━━━━━━━━━━━━━ 6:02 242ms/步 - 损失: 1.0646 - 平均绝对误差: 0.8256

99/1599 ━ [37m━━━━━━━━━━━━━━━━━━━ 6:03 242ms/步 - 损失: 1.0646 - 平均绝对误差: 0.8256

100/1599 ━ [37m━━━━━━━━━━━━━━━━━━━ 6:03 243ms/步 - 损失: 1.0645 - 平均绝对误差: 0.8256

101/1599 ━ [37m━━━━━━━━━━━━━━━━━━━ 6:03 243ms/步 - 损失: 1.0645 - 平均绝对误差: 0.8256

102/1599 ━ [37m━━━━━━━━━━━━━━━━━━━ 6:03 243ms/步 - 损失: 1.0645 - 平均绝对误差: 0.8256

103/1599 ━ [37m━━━━━━━━━━━━━━━━━━━ 6:03 243ms/步 - 损失: 1.0644 - 平均绝对误差: 0.8256

104/1599 ━ [37m━━━━━━━━━━━━━━━━━━━ 6:02 243ms/步 - 损失: 1.0644 - 平均绝对误差: 0.8256

105/1599 ━ [37m━━━━━━━━━━━━━━━━━━━ 6:02 243ms/步 - 损失: 1.0643 - 平均绝对误差: 0.8256

106/1599 ━ [37m━━━━━━━━━━━━━━━━━━━ 6:02 243ms/步 - 损失: 1.0643 - 平均绝对误差: 0.8256

107/1599 ━ [37m━━━━━━━━━━━━━━━━━━━ 6:02 243ms/步 - 损失: 1.0642 - 平均绝对误差: 0.8256

108/1599 ━ [37m━━━━━━━━━━━━━━━━━━━ 6:02 243ms/步 - 损失: 1.0642 - 平均绝对误差: 0.8256

109/1599 ━ [37m━━━━━━━━━━━━━━━━━━━ 6:01 243ms/步 - 损失: 1.0642 - 平均绝对误差: 0.8256

110/1599 ━ [37m━━━━━━━━━━━━━━━━━━━ 6:01 243ms/步 - 损失: 1.0642 - 平均绝对误差: 0.8256

111/1599 ━ [37m━━━━━━━━━━━━━━━━━━━ 6:01 243ms/步 - 损失: 1.0641 - 平均绝对误差: 0.8256

112/1599 ━ [37m━━━━━━━━━━━━━━━━━━━ 6:00 243ms/步 - 损失: 1.0641 - 平均绝对误差: 0.8256

113/1599 ━ [37m━━━━━━━━━━━━━━━━━━━ 6:00 243ms/步 - 损失: 1.0641 - 平均绝对误差: 0.8256

114/1599 ━ [37m━━━━━━━━━━━━━━━━━━━ 6:00 243ms/步 - 损失: 1.0640 - 平均绝对误差: 0.8256

115/1599 ━ [37m━━━━━━━━━━━━━━━━━━━ 6:00 243ms/步 - 损失: 1.0640 - 平均绝对误差: 0.8256

116/1599 ━ [37m━━━━━━━━━━━━━━━━━━━ 6:00 243ms/步 - 损失: 1.0640 - 平均绝对误差: 0.8256

117/1599 ━ [37m━━━━━━━━━━━━━━━━━━━ 6:00 243ms/步 - 损失: 1.0640 - 平均绝对误差: 0.8256

118/1599 ━ [37m━━━━━━━━━━━━━━━━━━━ 6:00 243ms/步 - 损失: 1.0639 - 平均绝对误差: 0.8256

119/1599 ━ [37m━━━━━━━━━━━━━━━━━━━ 5:59 243ms/步 - 损失: 1.0639 - 平均绝对误差: 0.8256

120/1599 ━ [37m━━━━━━━━━━━━━━━━━━━ 5:59 243ms/步 - 损失: 1.0639 - 平均绝对误差: 0.8256

121/1599 ━ [37m━━━━━━━━━━━━━━━━━━━ 5:59 243ms/步 - 损失: 1.0639 - 平均绝对误差: 0.8256

122/1599 ━ [37m━━━━━━━━━━━━━━━━━━━ 5:59 243ms/步 - 损失: 1.0639 - 平均绝对误差: 0.8256

123/1599 ━ [37m━━━━━━━━━━━━━━━━━━━ 5:59 243ms/步 - 损失: 1.0639 - 平均绝对误差: 0.8256

124/1599 ━ [37m━━━━━━━━━━━━━━━━━━━ 5:58 243ms/步 - 损失: 1.0638 - 平均绝对误差: 0.8256

125/1599 ━ [37m━━━━━━━━━━━━━━━━━━━ 5:58 243ms/步 - 损失: 1.0638 - 平均绝对误差: 0.8256

126/1599 ━ [37m━━━━━━━━━━━━━━━━━━━ 5:57 243ms/步 - 损失: 1.0638 - 平均绝对误差: 0.8256

127/1599 ━ [37m━━━━━━━━━━━━━━━━━━━ 5:57 243ms/步 - 损失: 1.0638 - 平均绝对误差: 0.8256

128/1599 ━ [37m━━━━━━━━━━━━━━━━━━━ 5:56 242ms/步 - 损失: 1.0638 - 平均绝对误差: 0.8256

129/1599 ━ [37m━━━━━━━━━━━━━━━━━━━ 5:56 242ms/步 - 损失: 1.0638 - 平均绝对误差: 0.8256

130/1599 ━ [37m━━━━━━━━━━━━━━━━━━━ 5:55 242ms/步 - 损失: 1.0637 - 平均绝对误差: 0.8256

131/1599 ━ [37m━━━━━━━━━━━━━━━━━━━ 5:54 242ms/步 - 损失: 1.0637 - 平均绝对误差: 0.8256

132/1599 ━ [37m━━━━━━━━━━━━━━━━━━━ 5:54 242ms/步 - 损失: 1.0637 - 平均绝对误差: 0.8256

133/1599 ━ [37m━━━━━━━━━━━━━━━━━━━ 5:54 242ms/步 - 损失: 1.0637 - 平均绝对误差: 0.8256

134/1599 ━ [37m━━━━━━━━━━━━━━━━━━━ 5:53 241ms/步 - 损失: 1.0636 - 平均绝对误差: 0.8255

135/1599 ━ [37m━━━━━━━━━━━━━━━━━━━ 5:53 241ms/步 - 损失: 1.0636 - 平均绝对误差: 0.8255

136/1599 ━ [37m━━━━━━━━━━━━━━━━━━━ 5:52 241ms/步 - 损失: 1.0636 - 平均绝对误差: 0.8255

137/1599 ━ [37m━━━━━━━━━━━━━━━━━━━ 5:52 241ms/步 - 损失: 1.0636 - 平均绝对误差: 0.8255

138/1599 ━ [37m━━━━━━━━━━━━━━━━━━━ 5:52 241ms/步 - 损失: 1.0635 - 平均绝对误差: 0.8255

139/1599 ━ [37m━━━━━━━━━━━━━━━━━━━ 5:51 241ms/步 - 损失: 1.0635 - 平均绝对误差: 0.8255

140/1599 ━ [37m━━━━━━━━━━━━━━━━━━━ 5:51 241ms/步 - 损失: 1.0635 - 平均绝对误差: 0.8255

141/1599 ━ [37m━━━━━━━━━━━━━━━━━━━ 5:51 241ms/步 - 损失: 1.0635 - 平均绝对误差: 0.8255

142/1599 ━ [37m━━━━━━━━━━━━━━━━━━━ 5:50 241ms/步 - 损失: 1.0634 - 平均绝对误差: 0.8255

143/1599 ━ [37m━━━━━━━━━━━━━━━━━━━ 5:50 241ms/步 - 损失: 1.0634 - 平均绝对误差: 0.8255

144/1599 ━ [37m━━━━━━━━━━━━━━━━━━━ 5:49 240ms/步 - 损失: 1.0634 - 平均绝对误差: 0.8255

145/1599 ━ [37m━━━━━━━━━━━━━━━━━━━ 5:49 240ms/步 - 损失: 1.0633 - 平均绝对误差: 0.8255

146/1599 ━ [37m━━━━━━━━━━━━━━━━━━━ 5:49 240ms/步 - 损失: 1.0633 - 平均绝对误差: 0.8255

147/1599 ━ [37m━━━━━━━━━━━━━━━━━━━ 5:49 241ms/步 - 损失: 1.0633 - 平均绝对误差: 0.8255

148/1599 ━ [37m━━━━━━━━━━━━━━━━━━━ 5:49 241ms/步 - 损失: 1.0632 - 平均绝对误差: 0.8254

149/1599 ━ [37m━━━━━━━━━━━━━━━━━━━ 5:49 241ms/步 - 损失: 1.0632 - 平均绝对误差: 0.8254

150/1599 ━ [37m━━━━━━━━━━━━━━━━━━━ 5:49 241ms/步 - 损失: 1.0632 - 平均绝对误差: 0.8254

151/1599 ━ [37m━━━━━━━━━━━━━━━━━━━ 5:49 241ms/步 - 损失: 1.0631 - 平均绝对误差: 0.8254

152/1599 ━ [37m━━━━━━━━━━━━━━━━━━━ 5:49 242ms/步 - 损失: 1.0631 - 平均绝对误差: 0.8254

153/1599 ━ [37m━━━━━━━━━━━━━━━━━━━ 5:49 242ms/步 - 损失: 1.0630 - 平均绝对误差: 0.8254

154/1599 ━ [37m━━━━━━━━━━━━━━━━━━━ 5:49 242ms/步 - 损失: 1.0630 - 平均绝对误差: 0.8254

155/1599 ━ [37m━━━━━━━━━━━━━━━━━━━ 5:49 242ms/步 - 损失: 1.0629 - 平均绝对误差: 0.8253

156/1599 ━ [37m━━━━━━━━━━━━━━━━━━━ 5:48 242ms/步 - 损失: 1.0629 - 平均绝对误差: 0.8253

157/1599 ━ [37m━━━━━━━━━━━━━━━━━━━ 5:48 242ms/步 - 损失: 1.0628 - 平均绝对误差: 0.8253

158/1599 ━ [37m━━━━━━━━━━━━━━━━━━━ 5:48 242ms/步 - 损失: 1.0627 - 平均绝对误差: 0.8253

159/1599 ━ [37m━━━━━━━━━━━━━━━━━━━ 5:47 242ms/步 - 损失: 1.0627 - 平均绝对误差: 0.8252

160/1599 ━━ [37m━━━━━━━━━━━━━━━━━━ 5:47 241ms/步 - 损失: 1.0626 - 平均绝对误差: 0.8252

161/1599 ━━ [37m━━━━━━━━━━━━━━━━━━ 5:46 241ms/步 - 损失: 1.0625 - 平均绝对误差: 0.8252

162/1599 ━━ [37m━━━━━━━━━━━━━━━━━━ 5:46 241ms/步 - 损失: 1.0625 - 平均绝对误差: 0.8252

163/1599 ━━ [37m━━━━━━━━━━━━━━━━━━ 5:46 241ms/步 - 损失: 1.0624 - 平均绝对误差: 0.8251

164/1599 ━━ [37m━━━━━━━━━━━━━━━━━━ 5:45 241ms/步 - 损失: 1.0623 - 平均绝对误差: 0.8251

165/1599 ━━ [37m━━━━━━━━━━━━━━━━━━ 5:45 241ms/步 - 损失: 1.0623 - 平均绝对误差: 0.8251

166/1599 ━━ [37m━━━━━━━━━━━━━━━━━━ 5:44 240ms/步 - 损失: 1.0622 - 平均绝对误差: 0.8251

167/1599 ━━ [37m━━━━━━━━━━━━━━━━━━ 5:43 240ms/步 - 损失: 1.0621 - 平均绝对误差: 0.8250

168/1599 ━━ [37m━━━━━━━━━━━━━━━━━━ 5:43 240ms/步 - 损失: 1.0621 - 平均绝对误差: 0.8250

169/1599 ━━ [37m━━━━━━━━━━━━━━━━━━ 5:43 240ms/步 - 损失: 1.0620 - 平均绝对误差: 0.8250

170/1599 ━━ [37m━━━━━━━━━━━━━━━━━━ 5:43 240ms/步 - 损失: 1.0619 - 平均绝对误差: 0.8250

171/1599 ━━ [37m━━━━━━━━━━━━━━━━━━ 5:42 240ms/步 - 损失: 1.0619 - 平均绝对误差: 0.8249

172/1599 ━━ [37m━━━━━━━━━━━━━━━━━━ 5:42 240ms/步 - 损失: 1.0618 - 平均绝对误差: 0.8249

173/1599 ━━ [37m━━━━━━━━━━━━━━━━━━ 5:42 240ms/步 - 损失: 1.0617 - 平均绝对误差: 0.8249

174/1599 ━━ [37m━━━━━━━━━━━━━━━━━━ 5:41 240ms/步 - 损失: 1.0617 - 平均绝对误差: 0.8249

175/1599 ━━ [37m━━━━━━━━━━━━━━━━━━ 5:41 240ms/步 - 损失: 1.0616 - 平均绝对误差: 0.8249

176/1599 ━━ [37m━━━━━━━━━━━━━━━━━━ 5:41 240ms/步 - 损失: 1.0616 - 平均绝对误差: 0.8248

177/1599 ━━ [37m━━━━━━━━━━━━━━━━━━ 5:40 240ms/步 - 损失: 1.0615 - 平均绝对误差: 0.8248

178/1599 ━━ [37m━━━━━━━━━━━━━━━━━━ 5:40 240ms/步 - 损失: 1.0614 - 平均绝对误差: 0.8248

179/1599 ━━ [37m━━━━━━━━━━━━━━━━━━ 5:40 239ms/步 - 损失: 1.0614 - 平均绝对误差: 0.8248

180/1599 ━━ [37m━━━━━━━━━━━━━━━━━━ 5:39 239ms/步 - 损失: 1.0613 - 平均绝对误差: 0.8247

181/1599 ━━ [37m━━━━━━━━━━━━━━━━━━ 5:39 240ms/步 - 损失: 1.0612 - 平均绝对误差: 0.8247

182/1599 ━━ [37m━━━━━━━━━━━━━━━━━━ 5:39 240ms/步 - 损失: 1.0612 - 平均绝对误差: 0.8247

183/1599 ━━ [37m━━━━━━━━━━━━━━━━━━ 5:39 240ms/步 - 损失: 1.0611 - 平均绝对误差: 0.8247

184/1599 ━━ [37m━━━━━━━━━━━━━━━━━━ 5:39 240ms/步 - 损失: 1.0611 - 平均绝对误差: 0.8247

185/1599 ━━ [37m━━━━━━━━━━━━━━━━━━ 5:39 240ms/步 - 损失: 1.0611 - 平均绝对误差: 0.8246

186/1599 ━━ [37m━━━━━━━━━━━━━━━━━━ 5:39 240ms/步 - 损失: 1.0610 - 平均绝对误差: 0.8246

187/1599 ━━ [37m━━━━━━━━━━━━━━━━━━ 5:39 240ms/步 - 损失: 1.0610 - 平均绝对误差: 0.8246

188/1599 ━━ [37m━━━━━━━━━━━━━━━━━━ 5:39 240ms/步 - 损失: 1.0609 - 平均绝对误差: 0.8246

189/1599 ━━ [37m━━━━━━━━━━━━━━━━━━ 5:38 240ms/步 - 损失: 1.0609 - 平均绝对误差: 0.8246

190/1599 ━━ [37m━━━━━━━━━━━━━━━━━━ 5:38 240ms/步 - 损失: 1.0608 - 平均绝对误差: 0.8246

191/1599 ━━ [37m━━━━━━━━━━━━━━━━━━ 5:38 240ms/步 - 损失: 1.0608 - 平均绝对误差: 0.8245

192/1599 ━━ [37m━━━━━━━━━━━━━━━━━━ 5:37 240ms/步 - 损失: 1.0608 - 平均绝对误差: 0.8245

193/1599 ━━ [37m━━━━━━━━━━━━━━━━━━ 5:37 240ms/步 - 损失: 1.0607 - 平均绝对误差: 0.8245

194/1599 ━━ [37m━━━━━━━━━━━━━━━━━━ 5:36 240ms/步 - 损失: 1.0607 - 平均绝对误差: 0.8245

195/1599 ━━ [37m━━━━━━━━━━━━━━━━━━ 5:36 240ms/步 - 损失: 1.0606 - 平均绝对误差: 0.8245

196/1599 ━━ [37m━━━━━━━━━━━━━━━━━━ 5:35 239ms/步 - 损失: 1.0606 - 平均绝对误差: 0.8245

197/1599 ━━ [37m━━━━━━━━━━━━━━━━━━ 5:35 239ms/步 - 损失: 1.0605 - 平均绝对误差: 0.8245

198/1599 ━━ [37m━━━━━━━━━━━━━━━━━━ 5:35 239ms/步 - 损失: 1.0605 - 平均绝对误差: 0.8244

199/1599 ━━ [37m━━━━━━━━━━━━━━━━━━ 5:34 239ms/步 - 损失: 1.0605 - 平均绝对误差: 0.8244

200/1599 ━━ [37m━━━━━━━━━━━━━━━━━━ 5:34 239ms/步 - 损失: 1.0604 - 平均绝对误差: 0.8244

201/1599 ━━ [37m━━━━━━━━━━━━━━━━━━ 5:34 239ms/步 - 损失: 1.0604 - 平均绝对误差: 0.8244

202/1599 ━━ [37m━━━━━━━━━━━━━━━━━━ 5:33 239ms/步 - 损失: 1.0603 - 平均绝对误差: 0.8244

203/1599 ━━ [37m━━━━━━━━━━━━━━━━━━ 5:33 239ms/步 - 损失: 1.0603 - 平均绝对误差: 0.8244

204/1599 ━━ [37m━━━━━━━━━━━━━━━━━━ 5:33 239ms/步 - 损失: 1.0602 - 平均绝对误差: 0.8243

205/1599 ━━ [37m━━━━━━━━━━━━━━━━━━ 5:33 239ms/步 - 损失: 1.0602 - 平均绝对误差: 0.8243

206/1599 ━━ [37m━━━━━━━━━━━━━━━━━━ 5:33 239ms/步 - 损失: 1.0601 - 平均绝对误差: 0.8243

207/1599 ━━ [37m━━━━━━━━━━━━━━━━━━ 5:32 239ms/步 - 损失: 1.0601 - 平均绝对误差: 0.8243

208/1599 ━━ [37m━━━━━━━━━━━━━━━━━━ 5:32 239ms/步 - 损失: 1.0600 - 平均绝对误差: 0.8243

209/1599 ━━ [37m━━━━━━━━━━━━━━━━━━ 5:32 239ms/步 - 损失: 1.0600 - 平均绝对误差: 0.8243

210/1599 ━━ [37m━━━━━━━━━━━━━━━━━━ 5:32 239ms/步 - 损失: 1.0599 - 平均绝对误差: 0.8242

211/1599 ━━ [37m━━━━━━━━━━━━━━━━━━ 5:32 239ms/步 - 损失: 1.0599 - 平均绝对误差: 0.8242

212/1599 ━━ [37m━━━━━━━━━━━━━━━━━━ 5:32 239ms/步 - 损失: 1.0598 - 平均绝对误差: 0.8242

213/1599 ━━ [37m━━━━━━━━━━━━━━━━━━ 5:32 240ms/步 - 损失: 1.0598 - 平均绝对误差: 0.8242

214/1599 ━━ [37m━━━━━━━━━━━━━━━━━━ 5:32 240ms/步 - 损失: 1.0597 - 平均绝对误差: 0.8242

215/1599 ━━ [37m━━━━━━━━━━━━━━━━━━ 5:32 240ms/步 - 损失: 1.0597 - 平均绝对误差: 0.8241

216/1599 ━━ [37m━━━━━━━━━━━━━━━━━━ 5:32 241ms/步 - 损失: 1.0596 - 平均绝对误差: 0.8241

217/1599 ━━ [37m━━━━━━━━━━━━━━━━━━ 5:32 241ms/步 - 损失: 1.0596 - 平均绝对误差: 0.8241

218/1599 ━━ [37m━━━━━━━━━━━━━━━━━━ 5:32 241ms/步 - 损失: 1.0595 - 平均绝对误差: 0.8241

219/1599 ━━ [37m━━━━━━━━━━━━━━━━━━ 5:32 241ms/步 - 损失: 1.0595 - 平均绝对误差: 0.8241

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221/1599 ━━ [37m━━━━━━━━━━━━━━━━━━ 5:33 242ms/步 - 损失: 1.0594 - 平均绝对误差: 0.8240

222/1599 ━━ [37m━━━━━━━━━━━━━━━━━━ 5:33 242ms/步 - 损失: 1.0593 - 平均绝对误差: 0.8240

223/1599 ━━ [37m━━━━━━━━━━━━━━━━━━ 5:32 242ms/步 - 损失: 1.0593 - 平均绝对误差: 0.8240

224/1599 ━━ [37m━━━━━━━━━━━━━━━━━━ 5:32 242ms/步 - 损失: 1.0592 - 平均绝对误差: 0.8240

225/1599 ━━ [37m━━━━━━━━━━━━━━━━━━ 5:32 242ms/步 - 损失: 1.0592 - 平均绝对误差: 0.8240

226/1599 ━━ [37m━━━━━━━━━━━━━━━━━━ 5:32 242ms/步 - 损失: 1.0591 - 平均绝对误差: 0.8240

227/1599 ━━ [37m━━━━━━━━━━━━━━━━━━ 5:31 242ms/步 - 损失: 1.0590 - 平均绝对误差: 0.8239

228/1599 ━━ [37m━━━━━━━━━━━━━━━━━━ 5:31 242ms/步 - 损失: 1.0590 - 平均绝对误差: 0.8239

229/1599 ━━ [37m━━━━━━━━━━━━━━━━━━ 5:31 242ms/步 - 损失: 1.0589 - 平均绝对误差: 0.8239

230/1599 ━━ [37m━━━━━━━━━━━━━━━━━━ 5:30 242ms/步 - 损失: 1.0589 - 平均绝对误差: 0.8239

231/1599 ━━ [37m━━━━━━━━━━━━━━━━━━ 5:30 242ms/步 - 损失: 1.0589 - 平均绝对误差: 0.8239

232/1599 ━━ [37m━━━━━━━━━━━━━━━━━━ 5:30 241ms/步 - 损失: 1.0588 - 平均绝对误差: 0.8238

233/1599 ━━ [37m━━━━━━━━━━━━━━━━━━ 5:29 241ms/步 - 损失: 1.0588 - 平均绝对误差: 0.8238

234/1599 ━━ [37m━━━━━━━━━━━━━━━━━━ 5:29 241ms/步 - 损失: 1.0587 - 平均绝对误差: 0.8238

235/1599 ━━ [37m━━━━━━━━━━━━━━━━━━ 5:28 241ms/步 - 损失: 1.0587 - 平均绝对误差: 0.8238

236/1599 ━━ [37m━━━━━━━━━━━━━━━━━━ 5:28 241ms/步 - 损失: 1.0586 - 平均绝对误差: 0.8238

237/1599 ━━ [37m━━━━━━━━━━━━━━━━━━ 5:28 241ms/步 - 损失: 1.0586 - 平均绝对误差: 0.8238

238/1599 ━━ [37m━━━━━━━━━━━━━━━━━━ 5:27 241ms/步 - 损失: 1.0585 - 平均绝对误差: 0.8237

239/1599 ━━ [37m━━━━━━━━━━━━━━━━━━ 5:27 241ms/步 - 损失: 1.0585 - 平均绝对误差: 0.8237

240/1599 ━━━ [37m━━━━━━━━━━━━━━━━━ 5:27 241ms/步 - 损失: 1.0584 - 平均绝对误差: 0.8237

241/1599 ━━━ [37m━━━━━━━━━━━━━━━━━ 5:27 241ms/步 - 损失: 1.0584 - 平均绝对误差: 0.8237

242/1599 ━━━ [37m━━━━━━━━━━━━━━━━━ 5:26 241ms/步 - 损失: 1.0583 - 平均绝对误差: 0.8237

243/1599 ━━━ [37m━━━━━━━━━━━━━━━━━ 5:26 241ms/步 - 损失: 1.0583 - 平均绝对误差: 0.8237

244/1599 ━━━ [37m━━━━━━━━━━━━━━━━━ 5:26 241ms/步 - 损失: 1.0583 - 平均绝对误差: 0.8236

245/1599 ━━━ [37m━━━━━━━━━━━━━━━━━ 5:26 241ms/步 - 损失: 1.0582 - 平均绝对误差: 0.8236

246/1599 ━━━ [37m━━━━━━━━━━━━━━━━━ 5:25 241ms/步 - 损失: 1.0582 - 平均绝对误差: 0.8236

247/1599 ━━━ [37m━━━━━━━━━━━━━━━━━ 5:25 241ms/步 - 损失: 1.0581 - 平均绝对误差: 0.8236

248/1599 ━━━ [37m━━━━━━━━━━━━━━━━━ 5:25 241ms/步 - 损失: 1.0581 - 平均绝对误差: 0.8236

249/1599 ━━━ [37m━━━━━━━━━━━━━━━━━ 5:25 241ms/步 - 损失: 1.0580 - 平均绝对误差: 0.8236

250/1599 ━━━ [37m━━━━━━━━━━━━━━━━━ 5:25 241ms/步 - 损失: 1.0580 - 平均绝对误差: 0.8235

251/1599 ━━━ [37m━━━━━━━━━━━━━━━━━ 5:25 241ms/步 - 损失: 1.0579 - 平均绝对误差: 0.8235

252/1599 ━━━ [37m━━━━━━━━━━━━━━━━━ 5:25 241ms/步 - 损失: 1.0578 - 平均绝对误差: 0.8235

253/1599 ━━━ [37m━━━━━━━━━━━━━━━━━ 5:24 241ms/步 - 损失: 1.0578 - 平均绝对误差: 0.8235

254/1599 ━━━ [37m━━━━━━━━━━━━━━━━━ 5:24 242ms/步 - 损失: 1.0577 - 平均绝对误差: 0.8235

255/1599 ━━━ [37m━━━━━━━━━━━━━━━━━ 5:24 242ms/步 - 损失: 1.0577 - 平均绝对误差: 0.8234

256/1599 ━━━ [37m━━━━━━━━━━━━━━━━━ 5:24 242ms/步 - 损失: 1.0576 - 平均绝对误差: 0.8234

257/1599 ━━━ [37m━━━━━━━━━━━━━━━━━ 5:24 242ms/步 - 损失: 1.0575 - 平均绝对误差: 0.8234

258/1599 ━━━ [37m━━━━━━━━━━━━━━━━━ 5:24 242ms/步 - 损失: 1.0575 - 平均绝对误差: 0.8234

259/1599 ━━━ [37m━━━━━━━━━━━━━━━━━ 5:23 242ms/步 - 损失: 1.0574 - 平均绝对误差: 0.8233

260/1599 ━━━ [37m━━━━━━━━━━━━━━━━━ 5:23 242ms/步 - 损失: 1.0573 - 平均绝对误差: 0.8233

261/1599 ━━━ [37m━━━━━━━━━━━━━━━━━ 5:23 242ms/步 - 损失: 1.0573 - 平均绝对误差: 0.8233

262/1599 ━━━ [37m━━━━━━━━━━━━━━━━━ 5:22 242ms/步 - 损失: 1.0572 - 平均绝对误差: 0.8233

263/1599 ━━━ [37m━━━━━━━━━━━━━━━━━ 5:22 242ms/步 - 损失: 1.0572 - 平均绝对误差: 0.8232

264/1599 ━━━ [37m━━━━━━━━━━━━━━━━━ 5:22 241ms/步 - 损失: 1.0571 - 平均绝对误差: 0.8232

265/1599 ━━━ [37m━━━━━━━━━━━━━━━━━ 5:22 241ms/步 - 损失: 1.0570 - 平均绝对误差: 0.8232

266/1599 ━━━ [37m━━━━━━━━━━━━━━━━━ 5:21 241ms/步 - 损失: 1.0569 - 平均绝对误差: 0.8232

267/1599 ━━━ [37m━━━━━━━━━━━━━━━━━ 5:21 241ms/步 - 损失: 1.0569 - 平均绝对误差: 0.8231

268/1599 ━━━ [37m━━━━━━━━━━━━━━━━━ 5:21 241ms/步 - 损失: 1.0568 - 平均绝对误差: 0.8231

269/1599 ━━━ [37m━━━━━━━━━━━━━━━━━ 5:21 242ms/步 - 损失: 1.0567 - 平均绝对误差: 0.8231

270/1599 ━━━ [37m━━━━━━━━━━━━━━━━━ 5:20 242ms/步 - 损失: 1.0567 - 平均绝对误差: 0.8230

271/1599 ━━━ [37m━━━━━━━━━━━━━━━━━ 5:20 242ms/步 - 损失: 1.0566 - 平均绝对误差: 0.8230

272/1599 ━━━ [37m━━━━━━━━━━━━━━━━━ 5:20 242ms/步 - 损失: 1.0565 - 平均绝对误差: 0.8230

273/1599 ━━━ [37m━━━━━━━━━━━━━━━━━ 5:20 241ms/步 - 损失: 1.0565 - 平均绝对误差: 0.8230

274/1599 ━━━ [37m━━━━━━━━━━━━━━━━━ 5:19 241ms/步 - 损失: 1.0564 - 平均绝对误差: 0.8229

275/1599 ━━━ [37m━━━━━━━━━━━━━━━━━ 5:19 241ms/步 - 损失: 1.0563 - 平均绝对误差: 0.8229

276/1599 ━━━ [37m━━━━━━━━━━━━━━━━━ 5:19 241ms/步 - 损失: 1.0563 - 平均绝对误差: 0.8229

277/1599 ━━━ [37m━━━━━━━━━━━━━━━━━ 5:18 241ms/步 - 损失: 1.0562 - 平均绝对误差: 0.8228

278/1599 ━━━ [37m━━━━━━━━━━━━━━━━━ 5:18 241ms/步 - 损失: 1.0561 - 平均绝对误差: 0.8228

279/1599 ━━━ [37m━━━━━━━━━━━━━━━━━ 5:18 241ms/步 - 损失: 1.0560 - 平均绝对误差: 0.8228

280/1599 ━━━ [37m━━━━━━━━━━━━━━━━━ 5:18 241ms/步 - 损失: 1.0560 - 平均绝对误差: 0.8228

281/1599 ━━━ [37m━━━━━━━━━━━━━━━━━ 5:17 241ms/步 - 损失: 1.0559 - 平均绝对误差: 0.8227

282/1599 ━━━ [37m━━━━━━━━━━━━━━━━━ 5:17 241ms/步 - 损失: 1.0558 - 平均绝对误差: 0.8227

283/1599 ━━━ [37m━━━━━━━━━━━━━━━━━ 5:17 241ms/步 - 损失: 1.0558 - 平均绝对误差: 0.8227

284/1599 ━━━ [37m━━━━━━━━━━━━━━━━━ 5:17 241ms/步 - 损失: 1.0557 - 平均绝对误差: 0.8227

285/1599 ━━━ [37m━━━━━━━━━━━━━━━━━ 5:17 241ms/步 - 损失: 1.0556 - 平均绝对误差: 0.8226

286/1599 ━━━ [37m━━━━━━━━━━━━━━━━━ 5:16 241ms/步 - 损失: 1.0556 - 平均绝对误差: 0.8226

287/1599 ━━━ [37m━━━━━━━━━━━━━━━━━ 5:16 241ms/步 - 损失: 1.0555 - 平均绝对误差: 0.8226

288/1599 ━━━ [37m━━━━━━━━━━━━━━━━━ 5:16 241ms/步 - 损失: 1.0554 - 平均绝对误差: 0.8225

289/1599 ━━━ [37m━━━━━━━━━━━━━━━━━ 5:16 241ms/步 - 损失: 1.0554 - 平均绝对误差: 0.8225

290/1599 ━━━ [37m━━━━━━━━━━━━━━━━━ 5:16 241ms/步 - 损失: 1.0553 - 平均绝对误差: 0.8225

291/1599 ━━━ [37m━━━━━━━━━━━━━━━━━ 5:15 241ms/步 - 损失: 1.0552 - 平均绝对误差: 0.8225

292/1599 ━━━ [37m━━━━━━━━━━━━━━━━━ 5:15 241ms/步 - 损失: 1.0552 - 平均绝对误差: 0.8224

293/1599 ━━━ [37m━━━━━━━━━━━━━━━━━ 5:15 241ms/步 - 损失: 1.0551 - 平均绝对误差: 0.8224

294/1599 ━━━ [37m━━━━━━━━━━━━━━━━━ 5:14 241ms/步 - 损失: 1.0550 - 平均绝对误差: 0.8224

295/1599 ━━━ [37m━━━━━━━━━━━━━━━━━ 5:14 241ms/步 - 损失: 1.0550 - 平均绝对误差: 0.8224

296/1599 ━━━ [37m━━━━━━━━━━━━━━━━━ 5:13 241ms/步 - 损失: 1.0549 - 平均绝对误差: 0.8223

297/1599 ━━━ [37m━━━━━━━━━━━━━━━━━ 5:13 241ms/步 - 损失: 1.0548 - 平均绝对误差: 0.8223

298/1599 ━━━ [37m━━━━━━━━━━━━━━━━━ 5:13 241ms/步 - 损失: 1.0548 - 平均绝对误差: 0.8223

299/1599 ━━━ [37m━━━━━━━━━━━━━━━━━ 5:12 241ms/步 - 损失: 1.0547 - 平均绝对误差: 0.8222

300/1599 ━━━ [37m━━━━━━━━━━━━━━━━━ 5:12 241ms/步 - 损失: 1.0546 - 平均绝对误差: 0.8222

301/1599 ━━━ [37m━━━━━━━━━━━━━━━━━ 5:12 240ms/步 - 损失: 1.0545 - 平均绝对误差: 0.8222

302/1599 ━━━ [37m━━━━━━━━━━━━━━━━━ 5:11 240ms/步 - 损失: 1.0545 - 平均绝对误差: 0.8222

303/1599 ━━━ [37m━━━━━━━━━━━━━━━━━ 5:11 240ms/步 - 损失: 1.0544 - 平均绝对误差: 0.8221

304/1599 ━━━ [37m━━━━━━━━━━━━━━━━━ 5:11 240ms/步 - 损失: 1.0543 - 平均绝对误差: 0.8221

305/1599 ━━━ [37m━━━━━━━━━━━━━━━━━ 5:10 240ms/步 - 损失: 1.0543 - 平均绝对误差: 0.8221

306/1599 ━━━ [37m━━━━━━━━━━━━━━━━━ 5:10 240ms/步 - 损失: 1.0542 - 平均绝对误差: 0.8220

307/1599 ━━━ [37m━━━━━━━━━━━━━━━━━ 5:10 240ms/步 - 损失: 1.0541 - 平均绝对误差: 0.8220

308/1599 ━━━ [37m━━━━━━━━━━━━━━━━━ 5:09 240ms/步 - 损失: 1.0541 - 平均绝对误差: 0.8220

309/1599 ━━━ [37m━━━━━━━━━━━━━━━━━ 5:09 240ms/步 - 损失: 1.0540 - 平均绝对误差: 0.8220

310/1599 ━━━ [37m━━━━━━━━━━━━━━━━━ 5:09 240ms/步 - 损失: 1.0539 - 平均绝对误差: 0.8219

311/1599 ━━━ [37m━━━━━━━━━━━━━━━━━ 5:09 240ms/步 - 损失: 1.0539 - 平均绝对误差: 0.8219

312/1599 ━━━ [37m━━━━━━━━━━━━━━━━━ 5:08 240ms/步 - 损失: 1.0538 - 平均绝对误差: 0.8219

313/1599 ━━━ [37m━━━━━━━━━━━━━━━━━ 5:08 240ms/步 - 损失: 1.0537 - 平均绝对误差: 0.8219

314/1599 ━━━ [37m━━━━━━━━━━━━━━━━━ 5:08 240ms/步 - 损失: 1.0536 - 平均绝对误差: 0.8218

315/1599 ━━━ [37m━━━━━━━━━━━━━━━━━ 5:08 240ms/步 - 损失: 1.0536 - 平均绝对误差: 0.8218

316/1599 ━━━ [37m━━━━━━━━━━━━━━━━━ 5:08 240ms/步 - 损失: 1.0535 - 平均绝对误差: 0.8218

317/1599 ━━━ [37m━━━━━━━━━━━━━━━━━ 5:08 240ms/步 - 损失: 1.0534 - 平均绝对误差: 0.8217

318/1599 ━━━ [37m━━━━━━━━━━━━━━━━━ 5:08 241ms/步 - 损失: 1.0534 - 平均绝对误差: 0.8217

319/1599 ━━━ [37m━━━━━━━━━━━━━━━━━ 5:07 241ms/步 - 损失: 1.0533 - 平均绝对误差: 0.8217

320/1599 ━━━━ [37m━━━━━━━━━━━━━━━━ 5:07 241ms/步 - 损失: 1.0532 - 平均绝对误差: 0.8217

321/1599 ━━━━ [37m━━━━━━━━━━━━━━━━ 5:07 241ms/步 - 损失: 1.0532 - 平均绝对误差: 0.8216

322/1599 ━━━━ [37m━━━━━━━━━━━━━━━━ 5:07 241ms/步 - 损失: 1.0531 - 平均绝对误差: 0.8216

323/1599 ━━━━ [37m━━━━━━━━━━━━━━━━ 5:07 241ms/步 - 损失: 1.0531 - 平均绝对误差: 0.8216

324/1599 ━━━━ [37m━━━━━━━━━━━━━━━━ 5:08 242ms/步 - 损失: 1.0530 - 平均绝对误差: 0.8216

325/1599 ━━━━ [37m━━━━━━━━━━━━━━━━ 5:08 242ms/步 - 损失: 1.0529 - 平均绝对误差: 0.8215

326/1599 ━━━━ [37m━━━━━━━━━━━━━━━━ 5:08 242ms/步 - 损失: 1.0529 - 平均绝对误差: 0.8215

327/1599 ━━━━ [37m━━━━━━━━━━━━━━━━ 5:07 242ms/步 - 损失: 1.0528 - 平均绝对误差: 0.8215

328/1599 ━━━━ [37m━━━━━━━━━━━━━━━━ 5:07 242ms/步 - 损失: 1.0527 - 平均绝对误差: 0.8214

329/1599 ━━━━ [37m━━━━━━━━━━━━━━━━ 5:07 242ms/步 - 损失: 1.0527 - 平均绝对误差: 0.8214

330/1599 ━━━━ [37m━━━━━━━━━━━━━━━━ 5:06 242ms/步 - 损失: 1.0526 - 平均绝对误差: 0.8214

331/1599 ━━━━ [37m━━━━━━━━━━━━━━━━ 5:06 242ms/步 - 损失: 1.0525 - 平均绝对误差: 0.8214

332/1599 ━━━━ [37m━━━━━━━━━━━━━━━━ 5:05 241ms/步 - 损失: 1.0525 - 平均绝对误差: 0.8213

333/1599 ━━━━ [37m━━━━━━━━━━━━━━━━ 5:05 241ms/步 - 损失: 1.0524 - 平均绝对误差: 0.8213

334/1599 ━━━━ [37m━━━━━━━━━━━━━━━━ 5:05 241ms/步 - 损失: 1.0523 - 平均绝对误差: 0.8213

335/1599 ━━━━ [37m━━━━━━━━━━━━━━━━ 5:05 241ms/步 - 损失: 1.0523 - 平均绝对误差: 0.8213

336/1599 ━━━━ [37m━━━━━━━━━━━━━━━━ 5:04 241ms/步 - 损失: 1.0522 - 平均绝对误差: 0.8212

337/1599 ━━━━ [37m━━━━━━━━━━━━━━━━ 5:04 241ms/步 - 损失: 1.0522 - 平均绝对误差: 0.8212

338/1599 ━━━━ [37m━━━━━━━━━━━━━━━━ 5:04 241ms/步 - 损失: 1.0521 - 平均绝对误差: 0.8212

339/1599 ━━━━ [37m━━━━━━━━━━━━━━━━ 5:03 241ms/步 - 损失: 1.0520 - 平均绝对误差: 0.8212

340/1599 ━━━━ [37m━━━━━━━━━━━━━━━━ 5:03 241ms/步 - 损失: 1.0520 - 平均绝对误差: 0.8211

341/1599 ━━━━ [37m━━━━━━━━━━━━━━━━ 5:03 241ms/步 - 损失: 1.0519 - 平均绝对误差: 0.8211

342/1599 ━━━━ [37m━━━━━━━━━━━━━━━━ 5:02 241ms/步 - 损失: 1.0518 - 平均绝对误差: 0.8211

343/1599 ━━━━ [37m━━━━━━━━━━━━━━━━ 5:02 241ms/步 - 损失: 1.0518 - 平均绝对误差: 0.8211

344/1599 ━━━━ [37m━━━━━━━━━━━━━━━━ 5:02 241ms/步 - 损失: 1.0517 - 平均绝对误差: 0.8210

345/1599 ━━━━ [37m━━━━━━━━━━━━━━━━ 5:02 241ms/步 - 损失: 1.0517 - 平均绝对误差: 0.8210

346/1599 ━━━━ [37m━━━━━━━━━━━━━━━━ 5:01 241ms/步 - 损失: 1.0516 - 平均绝对误差: 0.8210

347/1599 ━━━━ [37m━━━━━━━━━━━━━━━━ 5:01 241ms/步 - 损失: 1.0515 - 平均绝对误差: 0.8210

348/1599 ━━━━ [37m━━━━━━━━━━━━━━━━ 5:01 241ms/步 - 损失: 1.0515 - 平均绝对误差: 0.8209

349/1599 ━━━━ [37m━━━━━━━━━━━━━━━━ 5:01 241ms/步 - 损失: 1.0514 - 平均绝对误差: 0.8209

350/1599 ━━━━ [37m━━━━━━━━━━━━━━━━ 5:01 241ms/步 - 损失: 1.0514 - 平均绝对误差: 0.8209

351/1599 ━━━━ [37m━━━━━━━━━━━━━━━━ 5:01 241ms/步 - 损失: 1.0513 - 平均绝对误差: 0.8209

352/1599 ━━━━ [37m━━━━━━━━━━━━━━━━ 5:00 241ms/步 - 损失: 1.0513 - 平均绝对误差: 0.8208

353/1599 ━━━━ [37m━━━━━━━━━━━━━━━━ 5:00 241ms/步 - 损失: 1.0512 - 平均绝对误差: 0.8208

354/1599 ━━━━ [37m━━━━━━━━━━━━━━━━ 5:00 241ms/步 - 损失: 1.0511 - 平均绝对误差: 0.8208

355/1599 ━━━━ [37m━━━━━━━━━━━━━━━━ 4:59 241ms/步 - 损失: 1.0511 - 平均绝对误差: 0.8208

356/1599 ━━━━ [37m━━━━━━━━━━━━━━━━ 4:59 241ms/步 - 损失: 1.0510 - 平均绝对误差: 0.8208

357/1599 ━━━━ [37m━━━━━━━━━━━━━━━━ 4:59 241ms/步 - 损失: 1.0510 - 平均绝对误差: 0.8207

358/1599 ━━━━ [37m━━━━━━━━━━━━━━━━ 4:58 241ms/步 - 损失: 1.0509 - 平均绝对误差: 0.8207

359/1599 ━━━━ [37m━━━━━━━━━━━━━━━━ 4:58 241ms/步 - 损失: 1.0509 - 平均绝对误差: 0.8207

360/1599 ━━━━ [37m━━━━━━━━━━━━━━━━ 4:58 241ms/步 - 损失: 1.0508 - 平均绝对误差: 0.8207

361/1599 ━━━━ [37m━━━━━━━━━━━━━━━━ 4:57 241ms/步 - 损失: 1.0508 - 平均绝对误差: 0.8206

362/1599 ━━━━ [37m━━━━━━━━━━━━━━━━ 4:57 241ms/步 - 损失: 1.0507 - 平均绝对误差: 0.8206

363/1599 ━━━━ [37m━━━━━━━━━━━━━━━━ 4:57 240ms/步 - 损失: 1.0506 - 平均绝对误差: 0.8206

364/1599 ━━━━ [37m━━━━━━━━━━━━━━━━ 4:56 240ms/步 - 损失: 1.0506 - 平均绝对误差: 0.8206

365/1599 ━━━━ [37m━━━━━━━━━━━━━━━━ 4:56 240ms/步 - 损失: 1.0505 - 平均绝对误差: 0.8206

366/1599 ━━━━ [37m━━━━━━━━━━━━━━━━ 4:56 240ms/步 - 损失: 1.0505 - 平均绝对误差: 0.8205

367/1599 ━━━━ [37m━━━━━━━━━━━━━━━━ 4:56 240ms/步 - 损失: 1.0504 - 平均绝对误差: 0.8205

368/1599 ━━━━ [37m━━━━━━━━━━━━━━━━ 4:55 240ms/步 - 损失: 1.0504 - 平均绝对误差: 0.8205

369/1599 ━━━━ [37m━━━━━━━━━━━━━━━━ 4:55 240ms/步 - 损失: 1.0503 - 平均绝对误差: 0.8205

370/1599 ━━━━ [37m━━━━━━━━━━━━━━━━ 4:55 240ms/步 - 损失: 1.0503 - 平均绝对误差: 0.8204

371/1599 ━━━━ [37m━━━━━━━━━━━━━━━━ 4:54 240ms/步 - 损失: 1.0502 - 平均绝对误差: 0.8204

372/1599 ━━━━ [37m━━━━━━━━━━━━━━━━ 4:54 240ms/步 - 损失: 1.0502 - 平均绝对误差: 0.8204

373/1599 ━━━━ [37m━━━━━━━━━━━━━━━━ 4:54 240ms/步 - 损失: 1.0501 - 平均绝对误差: 0.8204

374/1599 ━━━━ [37m━━━━━━━━━━━━━━━━ 4:54 240ms/步 - 损失: 1.0501 - 平均绝对误差: 0.8204

375/1599 ━━━━ [37m━━━━━━━━━━━━━━━━ 4:53 240ms/步 - 损失: 1.0500 - 平均绝对误差: 0.8203

376/1599 ━━━━ [37m━━━━━━━━━━━━━━━━ 4:53 240ms/步 - 损失: 1.0500 - 平均绝对误差: 0.8203

377/1599 ━━━━ [37m━━━━━━━━━━━━━━━━ 4:53 240ms/步 - 损失: 1.0499 - 平均绝对误差: 0.8203

378/1599 ━━━━ [37m━━━━━━━━━━━━━━━━ 4:53 240ms/步 - 损失: 1.0499 - 平均绝对误差: 0.8203

379/1599 ━━━━ [37m━━━━━━━━━━━━━━━━ 4:53 240ms/步 - 损失: 1.0498 - 平均绝对误差: 0.8203

380/1599 ━━━━ [37m━━━━━━━━━━━━━━━━ 4:53 241ms/步 - 损失: 1.0498 - 平均绝对误差: 0.8203

381/1599 ━━━━ [37m━━━━━━━━━━━━━━━━ 4:53 241ms/步 - 损失: 1.0497 - 平均绝对误差: 0.8202

382/1599 ━━━━ [37m━━━━━━━━━━━━━━━━ 4:52 241ms/步 - 损失: 1.0497 - 平均绝对误差: 0.8202

383/1599 ━━━━ [37m━━━━━━━━━━━━━━━━ 4:52 241ms/步 - 损失: 1.0497 - 平均绝对误差: 0.8202

384/1599 ━━━━ [37m━━━━━━━━━━━━━━━━ 4:52 241ms/步 - 损失: 1.0496 - 平均绝对误差: 0.8202

385/1599 ━━━━ [37m━━━━━━━━━━━━━━━━ 4:52 241ms/步 - 损失: 1.0496 - 平均绝对误差: 0.8202

386/1599 ━━━━ [37m━━━━━━━━━━━━━━━━ 4:51 241ms/步 - 损失: 1.0495 - 平均绝对误差: 0.8201

387/1599 ━━━━ [37m━━━━━━━━━━━━━━━━ 4:51 241ms/步 - 损失: 1.0495 - 平均绝对误差: 0.8201

388/1599 ━━━━ [37m━━━━━━━━━━━━━━━━ 4:51 240ms/步 - 损失: 1.0494 - 平均绝对误差: 0.8201

389/1599 ━━━━ [37m━━━━━━━━━━━━━━━━ 4:50 240ms/步 - 损失: 1.0494 - 平均绝对误差: 0.8201

390/1599 ━━━━ [37m━━━━━━━━━━━━━━━━ 4:50 240ms/步 - 损失: 1.0493 - 平均绝对误差: 0.8201

391/1599 ━━━━ [37m━━━━━━━━━━━━━━━━ 4:50 240ms/步 - 损失: 1.0493 - 平均绝对误差: 0.8200

392/1599 ━━━━ [37m━━━━━━━━━━━━━━━━ 4:49 240ms/步 - 损失: 1.0492 - 平均绝对误差: 0.8200

393/1599 ━━━━ [37m━━━━━━━━━━━━━━━━ 4:49 240ms/步 - 损失: 1.0492 - 平均绝对误差: 0.8200

394/1599 ━━━━ [37m━━━━━━━━━━━━━━━━ 4:49 240ms/步 - 损失: 1.0491 - 平均绝对误差: 0.8200

395/1599 ━━━━ [37m━━━━━━━━━━━━━━━━ 4:48 240ms/步 - 损失: 1.0491 - 平均绝对误差: 0.8200

396/1599 ━━━━ [37m━━━━━━━━━━━━━━━━ 4:48 240ms/步 - 损失: 1.0491 - 平均绝对误差: 0.8200

397/1599 ━━━━ [37m━━━━━━━━━━━━━━━━ 4:48 240ms/步 - 损失: 1.0490 - 平均绝对误差: 0.8199

398/1599 ━━━━ [37m━━━━━━━━━━━━━━━━ 4:48 240ms/步 - 损失: 1.0490 - 平均绝对误差: 0.8199

399/1599 ━━━━ [37m━━━━━━━━━━━━━━━━ 4:47 240ms/步 - 损失: 1.0489 - 平均绝对误差: 0.8199

400/1599 ━━━━━ [37m━━━━━━━━━━━━━━━ 4:47 240ms/步 - 损失: 1.0489 - 平均绝对误差: 0.8199

401/1599 ━━━━━ [37m━━━━━━━━━━━━━━━ 4:47 240ms/步 - 损失: 1.0488 - 平均绝对误差: 0.8199

402/1599 ━━━━━ [37m━━━━━━━━━━━━━━━ 4:47 240ms/步 - 损失: 1.0488 - 平均绝对误差: 0.8198

403/1599 ━━━━━ [37m━━━━━━━━━━━━━━━ 4:46 240ms/步 - 损失: 1.0487 - 平均绝对误差: 0.8198

404/1599 ━━━━━ [37m━━━━━━━━━━━━━━━ 4:46 240ms/步 - 损失: 1.0487 - 平均绝对误差: 0.8198

405/1599 ━━━━━ [37m━━━━━━━━━━━━━━━ 4:46 240ms/步 - 损失: 1.0486 - 平均绝对误差: 0.8198

406/1599 ━━━━━ [37m━━━━━━━━━━━━━━━ 4:45 240ms/步 - 损失: 1.0486 - 平均绝对误差: 0.8198

407/1599 ━━━━━ [37m━━━━━━━━━━━━━━━ 4:45 240ms/步 - 损失: 1.0485 - 平均绝对误差: 0.8198

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424/1599 ━━━━━ [37m━━━━━━━━━━━━━━━ 4:42 241ms/步 - 损失: 1.0478 - 平均绝对误差: 0.8194

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431/1599 ━━━━━ [37m━━━━━━━━━━━━━━━ 4:41 241ms/步 - 损失: 1.0475 - 平均绝对误差: 0.8193

432/1599 ━━━━━ [37m━━━━━━━━━━━━━━━ 4:41 241ms/步 - 损失: 1.0474 - 平均绝对误差: 0.8193

433/1599 ━━━━━ [37m━━━━━━━━━━━━━━━ 4:40 241ms/步 - 损失: 1.0474 - 平均绝对误差: 0.8193

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441/1599 ━━━━━ [37m━━━━━━━━━━━━━━━ 4:39 241ms/步 - 损失: 1.0470 - 平均绝对误差: 0.8191

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443/1599 ━━━━━ [37m━━━━━━━━━━━━━━━ 4:38 241ms/步 - 损失: 1.0469 - 平均绝对误差: 0.8191

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447/1599 ━━━━━ [37m━━━━━━━━━━━━━━━ 4:38 241ms/步 - 损失: 1.0467 - 平均绝对误差: 0.8190

448/1599 ━━━━━ [37m━━━━━━━━━━━━━━━ 4:37 241ms/步 - 损失: 1.0467 - 平均绝对误差: 0.8190

449/1599 ━━━━━ [37m━━━━━━━━━━━━━━━ 4:37 241ms/步 - 损失: 1.0466 - 平均绝对误差: 0.8190

450/1599 ━━━━━ [37m━━━━━━━━━━━━━━━ 4:37 242ms/步 - 损失: 1.0466 - 平均绝对误差: 0.8190

451/1599 ━━━━━ [37m━━━━━━━━━━━━━━━ 4:37 242ms/步 - 损失: 1.0465 - 平均绝对误差: 0.8189

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453/1599 ━━━━━ [37m━━━━━━━━━━━━━━━ 4:37 242ms/步 - 损失: 1.0464 - 平均绝对误差: 0.8189

454/1599 ━━━━━ [37m━━━━━━━━━━━━━━━ 4:37 242ms/步 - 损失: 1.0464 - 平均绝对误差: 0.8189

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456/1599 ━━━━━ [37m━━━━━━━━━━━━━━━ 4:36 242ms/步 - 损失: 1.0463 - 平均绝对误差: 0.8188

457/1599 ━━━━━ [37m━━━━━━━━━━━━━━━ 4:36 242ms/步 - 损失: 1.0463 - 平均绝对误差: 0.8188

458/1599 ━━━━━ [37m━━━━━━━━━━━━━━━ 4:36 243ms/步 - 损失: 1.0462 - 平均绝对误差: 0.8188

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468/1599 ━━━━━ [37m━━━━━━━━━━━━━━━ 4:33 242ms/步 - 损失: 1.0457 - 平均绝对误差: 0.8186

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476/1599 ━━━━━ [37m━━━━━━━━━━━━━━━ 4:31 242ms/步 - 损失: 1.0453 - 平均绝对误差: 0.8184

477/1599 ━━━━━ [37m━━━━━━━━━━━━━━━ 4:31 242ms/步 - 损失: 1.0453 - 平均绝对误差: 0.8184

478/1599 ━━━━━ [37m━━━━━━━━━━━━━━━ 4:31 242ms/步 - 损失: 1.0452 - 平均绝对误差: 0.8184

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480/1599 ━━━━━━ [37m━━━━━━━━━━━━━━ 4:30 242ms/步 - 损失: 1.0451 - 平均绝对误差: 0.8184

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502/1599 ━━━━━━ [37m━━━━━━━━━━━━━━ 4:26 243ms/步 - 损失: 1.0441 - 平均绝对误差: 0.8179

503/1599 ━━━━━━ [37m━━━━━━━━━━━━━━ 4:26 243ms/步 - 损失: 1.0441 - 平均绝对误差: 0.8179

504/1599 ━━━━━━ [37m━━━━━━━━━━━━━━ 4:26 243ms/步 - 损失: 1.0440 - 平均绝对误差: 0.8179

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506/1599 ━━━━━━ [37m━━━━━━━━━━━━━━ 4:26 244ms/步 - 损失: 1.0440 - 平均绝对误差: 0.8179

507/1599 ━━━━━━ [37m━━━━━━━━━━━━━━ 4:26 244ms/步 - 损失: 1.0439 - 平均绝对误差: 0.8178

508/1599 ━━━━━━ [37m━━━━━━━━━━━━━━ 4:25 244ms/步 - 损失: 1.0439 - 平均绝对误差: 0.8178

509/1599 ━━━━━━ [37m━━━━━━━━━━━━━━ 4:25 244ms/步 - 损失: 1.0438 - 平均绝对误差: 0.8178

510/1599 ━━━━━━ [37m━━━━━━━━━━━━━━ 4:25 244ms/步 - 损失: 1.0438 - 平均绝对误差: 0.8178

511/1599 ━━━━━━ [37m━━━━━━━━━━━━━━ 4:25 244ms/步 - 损失: 1.0437 - 平均绝对误差: 0.8178

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513/1599 ━━━━━━ [37m━━━━━━━━━━━━━━ 4:24 244ms/步 - 损失: 1.0437 - 平均绝对误差: 0.8177

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515/1599 ━━━━━━ [37m━━━━━━━━━━━━━━ 4:24 244ms/步 - 损失: 1.0436 - 平均绝对误差: 0.8177

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517/1599 ━━━━━━ [37m━━━━━━━━━━━━━━ 4:23 244ms/步 - 损失: 1.0435 - 平均绝对误差: 0.8177

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519/1599 ━━━━━━ [37m━━━━━━━━━━━━━━ 4:23 244ms/步 - 损失: 1.0434 - 平均绝对误差: 0.8176

520/1599 ━━━━━━ [37m━━━━━━━━━━━━━━ 4:23 244ms/步 - 损失: 1.0434 - 平均绝对误差: 0.8176

521/1599 ━━━━━━ [37m━━━━━━━━━━━━━━ 4:22 244ms/步 - 损失: 1.0433 - 平均绝对误差: 0.8176

522/1599 ━━━━━━ [37m━━━━━━━━━━━━━━ 4:22 244ms/步 - 损失: 1.0433 - 平均绝对误差: 0.8176

523/1599 ━━━━━━ [37m━━━━━━━━━━━━━━ 4:22 244ms/步 - 损失: 1.0433 - 平均绝对误差: 0.8176

524/1599 ━━━━━━ [37m━━━━━━━━━━━━━━ 4:22 244ms/步 - 损失: 1.0432 - 平均绝对误差: 0.8176

525/1599 ━━━━━━ [37m━━━━━━━━━━━━━━ 4:21 244ms/步 - 损失: 1.0432 - 平均绝对误差: 0.8175

526/1599 ━━━━━━ [37m━━━━━━━━━━━━━━ 4:21 244ms/步 - 损失: 1.0431 - 平均绝对误差: 0.8175

527/1599 ━━━━━━ [37m━━━━━━━━━━━━━━ 4:21 244ms/步 - 损失: 1.0431 - 平均绝对误差: 0.8175

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529/1599 ━━━━━━ [37m━━━━━━━━━━━━━━ 4:20 244ms/步 - 损失: 1.0430 - 平均绝对误差: 0.8175

530/1599 ━━━━━━ [37m━━━━━━━━━━━━━━ 4:20 244ms/步 - 损失: 1.0430 - 平均绝对误差: 0.8175

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532/1599 ━━━━━━ [37m━━━━━━━━━━━━━━ 4:20 244ms/步 - 损失: 1.0429 - 平均绝对误差: 0.8174

533/1599 ━━━━━━ [37m━━━━━━━━━━━━━━ 4:20 244ms/步 - 损失: 1.0428 - 平均绝对误差: 0.8174

534/1599 ━━━━━━ [37m━━━━━━━━━━━━━━ 4:19 244ms/步 - 损失: 1.0428 - 平均绝对误差: 0.8174

535/1599 ━━━━━━ [37m━━━━━━━━━━━━━━ 4:19 244ms/步 - 损失: 1.0428 - 平均绝对误差: 0.8174

536/1599 ━━━━━━ [37m━━━━━━━━━━━━━━ 4:19 244ms/步 - 损失: 1.0427 - 平均绝对误差: 0.8174

537/1599 ━━━━━━ [37m━━━━━━━━━━━━━━ 4:19 244ms/步 - 损失: 1.0427 - 平均绝对误差: 0.8173

538/1599 ━━━━━━ [37m━━━━━━━━━━━━━━ 4:18 244ms/步 - 损失: 1.0426 - 平均绝对误差: 0.8173

539/1599 ━━━━━━ [37m━━━━━━━━━━━━━━ 4:18 244ms/步 - 损失: 1.0426 - 平均绝对误差: 0.8173

540/1599 ━━━━━━ [37m━━━━━━━━━━━━━━ 4:18 244ms/步 - 损失: 1.0426 - 平均绝对误差: 0.8173

541/1599 ━━━━━━ [37m━━━━━━━━━━━━━━ 4:18 244ms/步 - 损失: 1.0425 - 平均绝对误差: 0.8173

542/1599 ━━━━━━ [37m━━━━━━━━━━━━━━ 4:17 244ms/步 - 损失: 1.0425 - 平均绝对误差: 0.8173

543/1599 ━━━━━━ [37m━━━━━━━━━━━━━━ 4:17 244ms/步 - 损失: 1.0425 - 平均绝对误差: 0.8172

544/1599 ━━━━━━ [37m━━━━━━━━━━━━━━ 4:17 244ms/步 - 损失: 1.0424 - 平均绝对误差: 0.8172

545/1599 ━━━━━━ [37m━━━━━━━━━━━━━━ 4:17 244ms/步 - 损失: 1.0424 - 平均绝对误差: 0.8172

546/1599 ━━━━━━ [37m━━━━━━━━━━━━━━ 4:17 244ms/步 - 损失: 1.0423 - 平均绝对误差: 0.8172

547/1599 ━━━━━━ [37m━━━━━━━━━━━━━━ 4:16 244ms/步 - 损失: 1.0423 - 平均绝对误差: 0.8172

548/1599 ━━━━━━ [37m━━━━━━━━━━━━━━ 4:16 244ms/步 - 损失: 1.0423 - 平均绝对误差: 0.8172

549/1599 ━━━━━━ [37m━━━━━━━━━━━━━━ 4:16 244ms/步 - 损失: 1.0422 - 平均绝对误差: 0.8171

550/1599 ━━━━━━ [37m━━━━━━━━━━━━━━ 4:16 244ms/步 - 损失: 1.0422 - 平均绝对误差: 0.8171

551/1599 ━━━━━━ [37m━━━━━━━━━━━━━━ 4:15 244ms/步 - 损失: 1.0422 - 平均绝对误差: 0.8171

552/1599 ━━━━━━ [37m━━━━━━━━━━━━━━ 4:15 244ms/步 - 损失: 1.0421 - 平均绝对误差: 0.8171

553/1599 ━━━━━━ [37m━━━━━━━━━━━━━━ 4:15 244ms/步 - 损失: 1.0421 - 平均绝对误差: 0.8171

554/1599 ━━━━━━ [37m━━━━━━━━━━━━━━ 4:15 244ms/步 - 损失: 1.0420 - 平均绝对误差: 0.8171

555/1599 ━━━━━━ [37m━━━━━━━━━━━━━━ 4:15 244ms/步 - 损失: 1.0420 - 平均绝对误差: 0.8171

556/1599 ━━━━━━ [37m━━━━━━━━━━━━━━ 4:14 244ms/步 - 损失: 1.0420 - 平均绝对误差: 0.8170

557/1599 ━━━━━━ [37m━━━━━━━━━━━━━━ 4:14 244ms/步 - 损失: 1.0419 - 平均绝对误差: 0.8170

558/1599 ━━━━━━ [37m━━━━━━━━━━━━━━ 4:14 244ms/步 - 损失: 1.0419 - 平均绝对误差: 0.8170

559/1599 ━━━━━━ [37m━━━━━━━━━━━━━━ 4:14 244ms/步 - 损失: 1.0419 - 平均绝对误差: 0.8170

560/1599 ━━━━━━━ [37m━━━━━━━━━━━━━ 4:14 245ms/步 - 损失: 1.0418 - 平均绝对误差: 0.8170

561/1599 ━━━━━━━ [37m━━━━━━━━━━━━━ 4:13 245ms/步 - 损失: 1.0418 - 平均绝对误差: 0.8170

562/1599 ━━━━━━━ [37m━━━━━━━━━━━━━ 4:13 245ms/步 - 损失: 1.0417 - 平均绝对误差: 0.8169

563/1599 ━━━━━━━ [37m━━━━━━━━━━━━━ 4:13 245ms/步 - 损失: 1.0417 - 平均绝对误差: 0.8169

564/1599 ━━━━━━━ [37m━━━━━━━━━━━━━ 4:13 245ms/步 - 损失: 1.0417 - 平均绝对误差: 0.8169

565/1599 ━━━━━━━ [37m━━━━━━━━━━━━━ 4:13 245ms/步 - 损失: 1.0416 - 平均绝对误差: 0.8169

566/1599 ━━━━━━━ [37m━━━━━━━━━━━━━ 4:13 245ms/步 - 损失: 1.0416 - 平均绝对误差: 0.8169

567/1599 ━━━━━━━ [37m━━━━━━━━━━━━━ 4:13 245ms/步 - 损失: 1.0416 - 平均绝对误差: 0.8169

568/1599 ━━━━━━━ [37m━━━━━━━━━━━━━ 4:13 245ms/步 - 损失: 1.0415 - 平均绝对误差: 0.8169

569/1599 ━━━━━━━ [37m━━━━━━━━━━━━━ 4:12 245ms/步 - 损失: 1.0415 - 平均绝对误差: 0.8168

570/1599 ━━━━━━━ [37m━━━━━━━━━━━━━ 4:12 246ms/步 - 损失: 1.0415 - 平均绝对误差: 0.8168

571/1599 ━━━━━━━ [37m━━━━━━━━━━━━━ 4:12 246ms/步 - 损失: 1.0414 - 平均绝对误差: 0.8168

572/1599 ━━━━━━━ [37m━━━━━━━━━━━━━ 4:12 246ms/步 - 损失: 1.0414 - 平均绝对误差: 0.8168

573/1599 ━━━━━━━ [37m━━━━━━━━━━━━━ 4:11 246ms/步 - 损失: 1.0414 - 平均绝对误差: 0.8168

574/1599 ━━━━━━━ [37m━━━━━━━━━━━━━ 4:11 246ms/步 - 损失: 1.0413 - 平均绝对误差: 0.8168

575/1599 ━━━━━━━ [37m━━━━━━━━━━━━━ 4:11 246ms/步 - 损失: 1.0413 - 平均绝对误差: 0.8168

576/1599 ━━━━━━━ [37m━━━━━━━━━━━━━ 4:11 246ms/步 - 损失: 1.0413 - 平均绝对误差: 0.8167

577/1599 ━━━━━━━ [37m━━━━━━━━━━━━━ 4:11 246ms/步 - 损失: 1.0412 - 平均绝对误差: 0.8167

578/1599 ━━━━━━━ [37m━━━━━━━━━━━━━ 4:11 246ms/步 - 损失: 1.0412 - 平均绝对误差: 0.8167

579/1599 ━━━━━━━ [37m━━━━━━━━━━━━━ 4:10 246ms/步 - 损失: 1.0411 - 平均绝对误差: 0.8167

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585/1599 ━━━━━━━ [37m━━━━━━━━━━━━━ 4:09 247ms/步 - 损失: 1.0409 - 平均绝对误差: 0.8166

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589/1599 ━━━━━━━ [37m━━━━━━━━━━━━━ 4:09 247ms/步 - 损失: 1.0408 - 平均绝对误差: 0.8166

590/1599 ━━━━━━━ [37m━━━━━━━━━━━━━ 4:08 247ms/步 - 损失: 1.0408 - 平均绝对误差: 0.8165

591/1599 ━━━━━━━ [37m━━━━━━━━━━━━━ 4:08 247ms/步 - 损失: 1.0407 - 平均绝对误差: 0.8165

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594/1599 ━━━━━━━ [37m━━━━━━━━━━━━━ 4:08 247ms/步 - 损失: 1.0406 - 平均绝对误差: 0.8165

595/1599 ━━━━━━━ [37m━━━━━━━━━━━━━ 4:07 247ms/步 - 损失: 1.0406 - 平均绝对误差: 0.8165

596/1599 ━━━━━━━ [37m━━━━━━━━━━━━━ 4:07 247ms/步 - 损失: 1.0405 - 平均绝对误差: 0.8165

597/1599 ━━━━━━━ [37m━━━━━━━━━━━━━ 4:07 247ms/步 - 损失: 1.0405 - 平均绝对误差: 0.8164

598/1599 ━━━━━━━ [37m━━━━━━━━━━━━━ 4:07 247ms/步 - 损失: 1.0405 - 平均绝对误差: 0.8164

599/1599 ━━━━━━━ [37m━━━━━━━━━━━━━ 4:06 247ms/步 - 损失: 1.0404 - 平均绝对误差: 0.8164

600/1599 ━━━━━━━ [37m━━━━━━━━━━━━━ 4:06 247ms/步 - 损失: 1.0404 - 平均绝对误差: 0.8164

601/1599 ━━━━━━━ [37m━━━━━━━━━━━━━ 4:06 247ms/步 - 损失: 1.0404 - 平均绝对误差: 0.8164

602/1599 ━━━━━━━ [37m━━━━━━━━━━━━━ 4:06 247ms/步 - 损失: 1.0403 - 平均绝对误差: 0.8164

603/1599 ━━━━━━━ [37m━━━━━━━━━━━━━ 4:05 247ms/步 - 损失: 1.0403 - 平均绝对误差: 0.8164

604/1599 ━━━━━━━ [37m━━━━━━━━━━━━━ 4:05 247ms/步 - 损失: 1.0403 - 平均绝对误差: 0.8163

605/1599 ━━━━━━━ [37m━━━━━━━━━━━━━ 4:05 247ms/步 - 损失: 1.0402 - 平均绝对误差: 0.8163

606/1599 ━━━━━━━ [37m━━━━━━━━━━━━━ 4:05 247ms/步 - 损失: 1.0402 - 平均绝对误差: 0.8163

607/1599 ━━━━━━━ [37m━━━━━━━━━━━━━ 4:04 247ms/步 - 损失: 1.0402 - 平均绝对误差: 0.8163

608/1599 ━━━━━━━ [37m━━━━━━━━━━━━━ 4:04 247ms/步 - 损失: 1.0401 - 平均绝对误差: 0.8163

609/1599 ━━━━━━━ [37m━━━━━━━━━━━━━ 4:04 247ms/步 - 损失: 1.0401 - 平均绝对误差: 0.8163

610/1599 ━━━━━━━ [37m━━━━━━━━━━━━━ 4:04 247ms/步 - 损失: 1.0400 - 平均绝对误差: 0.8163

611/1599 ━━━━━━━ [37m━━━━━━━━━━━━━ 4:03 247ms/步 - 损失: 1.0400 - 平均绝对误差: 0.8162

612/1599 ━━━━━━━ [37m━━━━━━━━━━━━━ 4:03 247ms/步 - 损失: 1.0400 - 平均绝对误差: 0.8162

613/1599 ━━━━━━━ [37m━━━━━━━━━━━━━ 4:03 247ms/步 - 损失: 1.0399 - 平均绝对误差: 0.8162

614/1599 ━━━━━━━ [37m━━━━━━━━━━━━━ 4:03 247ms/步 - 损失: 1.0399 - 平均绝对误差: 0.8162

615/1599 ━━━━━━━ [37m━━━━━━━━━━━━━ 4:03 247ms/步 - 损失: 1.0399 - 平均绝对误差: 0.8162

616/1599 ━━━━━━━ [37m━━━━━━━━━━━━━ 4:02 247ms/步 - 损失: 1.0398 - 平均绝对误差: 0.8162

617/1599 ━━━━━━━ [37m━━━━━━━━━━━━━ 4:02 247ms/步 - 损失: 1.0398 - 平均绝对误差: 0.8162

618/1599 ━━━━━━━ [37m━━━━━━━━━━━━━ 4:02 247ms/步 - 损失: 1.0398 - 平均绝对误差: 0.8161

619/1599 ━━━━━━━ [37m━━━━━━━━━━━━━ 4:02 247ms/步 - 损失: 1.0397 - 平均绝对误差: 0.8161

620/1599 ━━━━━━━ [37m━━━━━━━━━━━━━ 4:01 247ms/步 - 损失: 1.0397 - 平均绝对误差: 0.8161

621/1599 ━━━━━━━ [37m━━━━━━━━━━━━━ 4:01 247ms/步 - 损失: 1.0397 - 平均绝对误差: 0.8161

622/1599 ━━━━━━━ [37m━━━━━━━━━━━━━ 4:01 247ms/步 - 损失: 1.0396 - 平均绝对误差: 0.8161

623/1599 ━━━━━━━ [37m━━━━━━━━━━━━━ 4:01 247ms/步 - 损失: 1.0396 - 平均绝对误差: 0.8161

624/1599 ━━━━━━━ [37m━━━━━━━━━━━━━ 4:01 247ms/步 - 损失: 1.0396 - 平均绝对误差: 0.8161

625/1599 ━━━━━━━ [37m━━━━━━━━━━━━━ 4:01 248ms/步 - 损失: 1.0395 - 平均绝对误差: 0.8161

626/1599 ━━━━━━━ [37m━━━━━━━━━━━━━ 4:00 248ms/步 - 损失: 1.0395 - 平均绝对误差: 0.8160

627/1599 ━━━━━━━ [37m━━━━━━━━━━━━━ 4:00 248ms/步 - 损失: 1.0395 - 平均绝对误差: 0.8160

628/1599 ━━━━━━━ [37m━━━━━━━━━━━━━ 4:00 248ms/步 - 损失: 1.0394 - 平均绝对误差: 0.8160

629/1599 ━━━━━━━ [37m━━━━━━━━━━━━━ 4:00 248ms/步 - 损失: 1.0394 - 平均绝对误差: 0.8160

630/1599 ━━━━━━━ [37m━━━━━━━━━━━━━ 3:59 248ms/步 - 损失: 1.0394 - 平均绝对误差: 0.8160

631/1599 ━━━━━━━ [37m━━━━━━━━━━━━━ 3:59 248ms/步 - 损失: 1.0394 - 平均绝对误差: 0.8160

632/1599 ━━━━━━━ [37m━━━━━━━━━━━━━ 3:59 247ms/步 - 损失: 1.0393 - 平均绝对误差: 0.8160

633/1599 ━━━━━━━ [37m━━━━━━━━━━━━━ 3:59 247ms/步 - 损失: 1.0393 - 平均绝对误差: 0.8159

634/1599 ━━━━━━━ [37m━━━━━━━━━━━━━ 3:58 247ms/步 - 损失: 1.0393 - 平均绝对误差: 0.8159

635/1599 ━━━━━━━ [37m━━━━━━━━━━━━━ 3:58 247ms/步 - 损失: 1.0392 - 平均绝对误差: 0.8159

636/1599 ━━━━━━━ [37m━━━━━━━━━━━━━ 3:58 247ms/步 - 损失: 1.0392 - 平均绝对误差: 0.8159

637/1599 ━━━━━━━ [37m━━━━━━━━━━━━━ 3:58 247ms/步 - 损失: 1.0392 - 平均绝对误差: 0.8159

638/1599 ━━━━━━━ [37m━━━━━━━━━━━━━ 3:57 247ms/步 - 损失: 1.0391 - 平均绝对误差: 0.8159

639/1599 ━━━━━━━ [37m━━━━━━━━━━━━━ 3:57 247ms/步 - 损失: 1.0391 - 平均绝对误差: 0.8159

640/1599 ━━━━━━━━ [37m━━━━━━━━━━━━ 3:57 247ms/步 - 损失: 1.0391 - 平均绝对误差: 0.8159

641/1599 ━━━━━━━━ [37m━━━━━━━━━━━━ 3:57 247ms/步 - 损失: 1.0390 - 平均绝对误差: 0.8158

642/1599 ━━━━━━━━ [37m━━━━━━━━━━━━ 3:56 247ms/步 - 损失: 1.0390 - 平均绝对误差: 0.8158

643/1599 ━━━━━━━━ [37m━━━━━━━━━━━━ 3:56 247ms/步 - 损失: 1.0390 - 平均绝对误差: 0.8158

644/1599 ━━━━━━━━ [37m━━━━━━━━━━━━ 3:56 247ms/步 - 损失: 1.0389 - 平均绝对误差: 0.8158

645/1599 ━━━━━━━━ [37m━━━━━━━━━━━━ 3:56 247ms/步 - 损失: 1.0389 - 平均绝对误差: 0.8158

646/1599 ━━━━━━━━ [37m━━━━━━━━━━━━ 3:55 247ms/步 - 损失: 1.0389 - 平均绝对误差: 0.8158

647/1599 ━━━━━━━━ [37m━━━━━━━━━━━━ 3:55 248ms/步 - 损失: 1.0389 - 平均绝对误差: 0.8158

648/1599 ━━━━━━━━ [37m━━━━━━━━━━━━ 3:55 248ms/步 - 损失: 1.0388 - 平均绝对误差: 0.8158

649/1599 ━━━━━━━━ [37m━━━━━━━━━━━━ 3:55 248ms/步 - 损失: 1.0388 - 平均绝对误差: 0.8157

650/1599 ━━━━━━━━ [37m━━━━━━━━━━━━ 3:55 248ms/步 - 损失: 1.0388 - 平均绝对误差: 0.8157

651/1599 ━━━━━━━━ [37m━━━━━━━━━━━━ 3:54 248ms/步 - 损失: 1.0387 - 平均绝对误差: 0.8157

652/1599 ━━━━━━━━ [37m━━━━━━━━━━━━ 3:54 248ms/步 - 损失: 1.0387 - 平均绝对误差: 0.8157

653/1599 ━━━━━━━━ [37m━━━━━━━━━━━━ 3:54 248ms/步 - 损失: 1.0387 - 平均绝对误差: 0.8157

654/1599 ━━━━━━━━ [37m━━━━━━━━━━━━ 3:54 248ms/步 - 损失: 1.0386 - 平均绝对误差: 0.8157

655/1599 ━━━━━━━━ [37m━━━━━━━━━━━━ 3:54 248ms/步 - 损失: 1.0386 - 平均绝对误差: 0.8157

656/1599 ━━━━━━━━ [37m━━━━━━━━━━━━ 3:54 248ms/步 - 损失: 1.0386 - 平均绝对误差: 0.8157

657/1599 ━━━━━━━━ [37m━━━━━━━━━━━━ 3:53 248ms/步 - 损失: 1.0386 - 平均绝对误差: 0.8156

658/1599 ━━━━━━━━ [37m━━━━━━━━━━━━ 3:53 248ms/步 - 损失: 1.0385 - 平均绝对误差: 0.8156

659/1599 ━━━━━━━━ [37m━━━━━━━━━━━━ 3:53 248ms/步 - 损失: 1.0385 - 平均绝对误差: 0.8156

660/1599 ━━━━━━━━ [37m━━━━━━━━━━━━ 3:53 248ms/步 - 损失: 1.0385 - 平均绝对误差: 0.8156

661/1599 ━━━━━━━━ [37m━━━━━━━━━━━━ 3:52 248ms/步 - 损失: 1.0384 - 平均绝对误差: 0.8156

662/1599 ━━━━━━━━ [37m━━━━━━━━━━━━ 3:52 248ms/步 - 损失: 1.0384 - 平均绝对误差: 0.8156

663/1599 ━━━━━━━━ [37m━━━━━━━━━━━━ 3:52 248ms/步 - 损失: 1.0384 - 平均绝对误差: 0.8156

664/1599 ━━━━━━━━ [37m━━━━━━━━━━━━ 3:52 248ms/步 - 损失: 1.0384 - 平均绝对误差: 0.8156

665/1599 ━━━━━━━━ [37m━━━━━━━━━━━━ 3:51 248ms/步 - 损失: 1.0383 - 平均绝对误差: 0.8155

666/1599 ━━━━━━━━ [37m━━━━━━━━━━━━ 3:51 248ms/步 - 损失: 1.0383 - 平均绝对误差: 0.8155

667/1599 ━━━━━━━━ [37m━━━━━━━━━━━━ 3:51 248ms/步 - 损失: 1.0383 - 平均绝对误差: 0.8155

668/1599 ━━━━━━━━ [37m━━━━━━━━━━━━ 3:51 248ms/步 - 损失: 1.0382 - 平均绝对误差: 0.8155

669/1599 ━━━━━━━━ [37m━━━━━━━━━━━━ 3:50 248ms/步 - 损失: 1.0382 - 平均绝对误差: 0.8155

670/1599 ━━━━━━━━ [37m━━━━━━━━━━━━ 3:50 248ms/步 - 损失: 1.0382 - 平均绝对误差: 0.8155

671/1599 ━━━━━━━━ [37m━━━━━━━━━━━━ 3:50 248ms/步 - 损失: 1.0382 - 平均绝对误差: 0.8155

672/1599 ━━━━━━━━ [37m━━━━━━━━━━━━ 3:50 248ms/步 - 损失: 1.0381 - 平均绝对误差: 0.8155

673/1599 ━━━━━━━━ [37m━━━━━━━━━━━━ 3:49 248ms/步 - 损失: 1.0381 - 平均绝对误差: 0.8155

674/1599 ━━━━━━━━ [37m━━━━━━━━━━━━ 3:49 248ms/步 - 损失: 1.0381 - 平均绝对误差: 0.8154

675/1599 ━━━━━━━━ [37m━━━━━━━━━━━━ 3:49 248ms/步 - 损失: 1.0380 - 平均绝对误差: 0.8154

676/1599 ━━━━━━━━ [37m━━━━━━━━━━━━ 3:49 248ms/步 - 损失: 1.0380 - 平均绝对误差: 0.8154

677/1599 ━━━━━━━━ [37m━━━━━━━━━━━━ 3:48 248ms/步 - 损失: 1.0380 - 平均绝对误差: 0.8154

678/1599 ━━━━━━━━ [37m━━━━━━━━━━━━ 3:48 248ms/步 - 损失: 1.0380 - 平均绝对误差: 0.8154

679/1599 ━━━━━━━━ [37m━━━━━━━━━━━━ 3:48 248ms/步 - 损失: 1.0379 - 平均绝对误差: 0.8154

680/1599 ━━━━━━━━ [37m━━━━━━━━━━━━ 3:48 248ms/步 - 损失: 1.0379 - 平均绝对误差: 0.8154

681/1599 ━━━━━━━━ [37m━━━━━━━━━━━━ 3:48 248ms/步 - 损失: 1.0379 - 平均绝对误差: 0.8154

682/1599 ━━━━━━━━ [37m━━━━━━━━━━━━ 3:47 249ms/步 - 损失: 1.0379 - 平均绝对误差: 0.8154

683/1599 ━━━━━━━━ [37m━━━━━━━━━━━━ 3:47 249ms/步 - 损失: 1.0378 - 平均绝对误差: 0.8153

684/1599 ━━━━━━━━ [37m━━━━━━━━━━━━ 3:47 249ms/步 - 损失: 1.0378 - 平均绝对误差: 0.8153

685/1599 ━━━━━━━━ [37m━━━━━━━━━━━━ 3:47 249ms/步 - 损失: 1.0378 - 平均绝对误差: 0.8153

686/1599 ━━━━━━━━ [37m━━━━━━━━━━━━ 3:46 249ms/步 - 损失: 1.0378 - 平均绝对误差: 0.8153

687/1599 ━━━━━━━━ [37m━━━━━━━━━━━━ 3:46 249ms/步 - 损失: 1.0377 - 平均绝对误差: 0.8153

688/1599 ━━━━━━━━ [37m━━━━━━━━━━━━ 3:46 249ms/步 - 损失: 1.0377 - 平均绝对误差: 0.8153

689/1599 ━━━━━━━━ [37m━━━━━━━━━━━━ 3:46 249ms/步 - 损失: 1.0377 - 平均绝对误差: 0.8153

690/1599 ━━━━━━━━ [37m━━━━━━━━━━━━ 3:45 249ms/步 - 损失: 1.0376 - 平均绝对误差: 0.8153

691/1599 ━━━━━━━━ [37m━━━━━━━━━━━━ 3:45 249ms/步 - 损失: 1.0376 - 平均绝对误差: 0.8153

692/1599 ━━━━━━━━ [37m━━━━━━━━━━━━ 3:45 249ms/步 - 损失: 1.0376 - 平均绝对误差: 0.8152

693/1599 ━━━━━━━━ [37m━━━━━━━━━━━━ 3:45 249ms/步 - 损失: 1.0376 - 平均绝对误差: 0.8152

694/1599 ━━━━━━━━ [37m━━━━━━━━━━━━ 3:44 249ms/步 - 损失: 1.0375 - 平均绝对误差: 0.8152

695/1599 ━━━━━━━━ [37m━━━━━━━━━━━━ 3:44 249ms/步 - 损失: 1.0375 - 平均绝对误差: 0.8152

696/1599 ━━━━━━━━ [37m━━━━━━━━━━━━ 3:44 249ms/步 - 损失: 1.0375 - 平均绝对误差: 0.8152

697/1599 ━━━━━━━━ [37m━━━━━━━━━━━━ 3:44 249ms/步 - 损失: 1.0375 - 平均绝对误差: 0.8152

698/1599 ━━━━━━━━ [37m━━━━━━━━━━━━ 3:43 249ms/步 - 损失: 1.0374 - 平均绝对误差: 0.8152

699/1599 ━━━━━━━━ [37m━━━━━━━━━━━━ 3:43 249ms/步 - 损失: 1.0374 - 平均绝对误差: 0.8152

700/1599 ━━━━━━━━ [37m━━━━━━━━━━━━ 3:43 249ms/步 - 损失: 1.0374 - 平均绝对误差: 0.8152

701/1599 ━━━━━━━━ [37m━━━━━━━━━━━━ 3:43 249ms/步 - 损失: 1.0374 - 平均绝对误差: 0.8151

702/1599 ━━━━━━━━ [37m━━━━━━━━━━━━ 3:42 249ms/步 - 损失: 1.0373 - 平均绝对误差: 0.8151

703/1599 ━━━━━━━━ [37m━━━━━━━━━━━━ 3:42 249ms/步 - 损失: 1.0373 - 平均绝对误差: 0.8151

704/1599 ━━━━━━━━ [37m━━━━━━━━━━━━ 3:42 249ms/步 - 损失: 1.0373 - 平均绝对误差: 0.8151

705/1599 ━━━━━━━━ [37m━━━━━━━━━━━━ 3:42 249ms/步 - 损失: 1.0373 - 平均绝对误差: 0.8151

706/1599 ━━━━━━━━ [37m━━━━━━━━━━━━ 3:41 249ms/步 - 损失: 1.0372 - 平均绝对误差: 0.8151

707/1599 ━━━━━━━━ [37m━━━━━━━━━━━━ 3:41 249ms/步 - 损失: 1.0372 - 平均绝对误差: 0.8151

708/1599 ━━━━━━━━ [37m━━━━━━━━━━━━ 3:41 249ms/步 - 损失: 1.0372 - 平均绝对误差: 0.8151

709/1599 ━━━━━━━━ [37m━━━━━━━━━━━━ 3:41 249ms/步 - 损失: 1.0372 - 平均绝对误差: 0.8151

710/1599 ━━━━━━━━ [37m━━━━━━━━━━━━ 3:41 249ms/步 - 损失: 1.0371 - 平均绝对误差: 0.8150

711/1599 ━━━━━━━━ [37m━━━━━━━━━━━━ 3:41 249ms/步 - 损失: 1.0371 - 平均绝对误差: 0.8150

712/1599 ━━━━━━━━ [37m━━━━━━━━━━━━ 3:40 249ms/步 - 损失: 1.0371 - 平均绝对误差: 0.8150

713/1599 ━━━━━━━━ [37m━━━━━━━━━━━━ 3:40 249ms/步 - 损失: 1.0370 - 平均绝对误差: 0.8150

714/1599 ━━━━━━━━ [37m━━━━━━━━━━━━ 3:40 249ms/步 - 损失: 1.0370 - 平均绝对误差: 0.8150

715/1599 ━━━━━━━━ [37m━━━━━━━━━━━━ 3:40 249ms/步 - 损失: 1.0370 - 平均绝对误差: 0.8150

716/1599 ━━━━━━━━ [37m━━━━━━━━━━━━ 3:39 249ms/步 - 损失: 1.0370 - 平均绝对误差: 0.8150

717/1599 ━━━━━━━━ [37m━━━━━━━━━━━━ 3:39 249ms/步 - 损失: 1.0369 - 平均绝对误差: 0.8150

718/1599 ━━━━━━━━ [37m━━━━━━━━━━━━ 3:39 249ms/步 - 损失: 1.0369 - 平均绝对误差: 0.8150

719/1599 ━━━━━━━━ [37m━━━━━━━━━━━━ 3:38 249ms/步 - 损失: 1.0369 - 平均绝对误差: 0.8150

720/1599 ━━━━━━━━━ [37m━━━━━━━━━━━ 3:38 249ms/步 - 损失: 1.0369 - 平均绝对误差: 0.8149

721/1599 ━━━━━━━━━ [37m━━━━━━━━━━━ 3:38 249ms/步 - 损失: 1.0368 - 平均绝对误差: 0.8149

722/1599 ━━━━━━━━━ [37m━━━━━━━━━━━ 3:38 249ms/步 - 损失: 1.0368 - 平均绝对误差: 0.8149

723/1599 ━━━━━━━━━ [37m━━━━━━━━━━━ 3:37 249ms/步 - 损失: 1.0368 - 平均绝对误差: 0.8149

724/1599 ━━━━━━━━━ [37m━━━━━━━━━━━ 3:37 249ms/步 - 损失: 1.0368 - 平均绝对误差: 0.8149

725/1599 ━━━━━━━━━ [37m━━━━━━━━━━━ 3:37 249ms/步 - 损失: 1.0368 - 平均绝对误差: 0.8149

726/1599 ━━━━━━━━━ [37m━━━━━━━━━━━ 3:37 249ms/步 - 损失: 1.0367 - 平均绝对误差: 0.8149

727/1599 ━━━━━━━━━ [37m━━━━━━━━━━━ 3:37 249ms/步 - 损失: 1.0367 - 平均绝对误差: 0.8149

728/1599 ━━━━━━━━━ [37m━━━━━━━━━━━ 3:37 249ms/步 - 损失: 1.0367 - 平均绝对误差: 0.8149

729/1599 ━━━━━━━━━ [37m━━━━━━━━━━━ 3:37 249ms/步 - 损失: 1.0367 - 平均绝对误差: 0.8148

730/1599 ━━━━━━━━━ [37m━━━━━━━━━━━ 3:36 249ms/步 - 损失: 1.0366 - 平均绝对误差: 0.8148

731/1599 ━━━━━━━━━ [37m━━━━━━━━━━━ 3:36 249ms/步 - 损失: 1.0366 - 平均绝对误差: 0.8148

732/1599 ━━━━━━━━━ [37m━━━━━━━━━━━ 3:36 249ms/步 - 损失: 1.0366 - 平均绝对误差: 0.8148

733/1599 ━━━━━━━━━ [37m━━━━━━━━━━━ 3:35 249ms/步 - 损失: 1.0366 - 平均绝对误差: 0.8148

734/1599 ━━━━━━━━━ [37m━━━━━━━━━━━ 3:35 249ms/步 - 损失: 1.0365 - 平均绝对误差: 0.8148

735/1599 ━━━━━━━━━ [37m━━━━━━━━━━━ 3:35 249ms/步 - 损失: 1.0365 - 平均绝对误差: 0.8148

736/1599 ━━━━━━━━━ [37m━━━━━━━━━━━ 3:35 249ms/步 - 损失: 1.0365 - 平均绝对误差: 0.8148

737/1599 ━━━━━━━━━ [37m━━━━━━━━━━━ 3:34 249ms/步 - 损失: 1.0365 - 平均绝对误差: 0.8148

738/1599 ━━━━━━━━━ [37m━━━━━━━━━━━ 3:34 249ms/步 - 损失: 1.0364 - 平均绝对误差: 0.8148

739/1599 ━━━━━━━━━ [37m━━━━━━━━━━━ 3:34 249ms/步 - 损失: 1.0364 - 平均绝对误差: 0.8147

740/1599 ━━━━━━━━━ [37m━━━━━━━━━━━ 3:34 249ms/步 - 损失: 1.0364 - 平均绝对误差: 0.8147

741/1599 ━━━━━━━━━ [37m━━━━━━━━━━━ 3:33 249ms/步 - 损失: 1.0364 - 平均绝对误差: 0.8147

742/1599 ━━━━━━━━━ [37m━━━━━━━━━━━ 3:33 249ms/步 - 损失: 1.0363 - 平均绝对误差: 0.8147

743/1599 ━━━━━━━━━ [37m━━━━━━━━━━━ 3:33 249ms/步 - 损失: 1.0363 - 平均绝对误差: 0.8147

744/1599 ━━━━━━━━━ [37m━━━━━━━━━━━ 3:33 249ms/步 - 损失: 1.0363 - 平均绝对误差: 0.8147

745/1599 ━━━━━━━━━ [37m━━━━━━━━━━━ 3:32 249ms/步 - 损失: 1.0363 - 平均绝对误差: 0.8147

746/1599 ━━━━━━━━━ [37m━━━━━━━━━━━ 3:32 249ms/步 - 损失: 1.0363 - 平均绝对误差: 0.8147

747/1599 ━━━━━━━━━ [37m━━━━━━━━━━━ 3:32 249ms/步 - 损失: 1.0362 - 平均绝对误差: 0.8147

748/1599 ━━━━━━━━━ [37m━━━━━━━━━━━ 3:32 249ms/步 - 损失: 1.0362 - 平均绝对误差: 0.8147

749/1599 ━━━━━━━━━ [37m━━━━━━━━━━━ 3:32 249ms/步 - 损失: 1.0362 - 平均绝对误差: 0.8146

750/1599 ━━━━━━━━━ [37m━━━━━━━━━━━ 3:31 250ms/步 - 损失: 1.0362 - 平均绝对误差: 0.8146

751/1599 ━━━━━━━━━ [37m━━━━━━━━━━━ 3:31 250ms/步 - 损失: 1.0361 - 平均绝对误差: 0.8146

752/1599 ━━━━━━━━━ [37m━━━━━━━━━━━ 3:31 250ms/步 - 损失: 1.0361 - 平均绝对误差: 0.8146

753/1599 ━━━━━━━━━ [37m━━━━━━━━━━━ 3:31 250ms/步 - 损失: 1.0361 - 平均绝对误差: 0.8146

754/1599 ━━━━━━━━━ [37m━━━━━━━━━━━ 3:31 250ms/步 - 损失: 1.0361 - 平均绝对误差: 0.8146

755/1599 ━━━━━━━━━ [37m━━━━━━━━━━━ 3:30 250ms/步 - 损失: 1.0360 - 平均绝对误差: 0.8146

756/1599 ━━━━━━━━━ [37m━━━━━━━━━━━ 3:30 250ms/步 - 损失: 1.0360 - 平均绝对误差: 0.8146

757/1599 ━━━━━━━━━ [37m━━━━━━━━━━━ 3:30 250ms/步 - 损失: 1.0360 - 平均绝对误差: 0.8146

758/1599 ━━━━━━━━━ [37m━━━━━━━━━━━ 3:29 250ms/步 - 损失: 1.0360 - 平均绝对误差: 0.8146

759/1599 ━━━━━━━━━ [37m━━━━━━━━━━━ 3:29 250ms/步 - 损失: 1.0360 - 平均绝对误差: 0.8146

760/1599 ━━━━━━━━━ [37m━━━━━━━━━━━ 3:29 250ms/步 - 损失: 1.0359 - 平均绝对误差: 0.8145

761/1599 ━━━━━━━━━ [37m━━━━━━━━━━━ 3:29 250ms/步 - 损失: 1.0359 - 平均绝对误差: 0.8145

762/1599 ━━━━━━━━━ [37m━━━━━━━━━━━ 3:29 250ms/步 - 损失: 1.0359 - 平均绝对误差: 0.8145

763/1599 ━━━━━━━━━ [37m━━━━━━━━━━━ 3:28 250ms/步 - 损失: 1.0359 - 平均绝对误差: 0.8145

764/1599 ━━━━━━━━━ [37m━━━━━━━━━━━ 3:28 250ms/步 - 损失: 1.0358 - 平均绝对误差: 0.8145

765/1599 ━━━━━━━━━ [37m━━━━━━━━━━━ 3:28 250ms/步 - 损失: 1.0358 - 平均绝对误差: 0.8145

766/1599 ━━━━━━━━━ [37m━━━━━━━━━━━ 3:28 250ms/步 - 损失: 1.0358 - 平均绝对误差: 0.8145

767/1599 ━━━━━━━━━ [37m━━━━━━━━━━━ 3:27 250ms/步 - 损失: 1.0358 - 平均绝对误差: 0.8145

768/1599 ━━━━━━━━━ [37m━━━━━━━━━━━ 3:27 250ms/步 - 损失: 1.0358 - 平均绝对误差: 0.8145

769/1599 ━━━━━━━━━ [37m━━━━━━━━━━━ 3:27 250ms/步 - 损失: 1.0357 - 平均绝对误差: 0.8145

770/1599 ━━━━━━━━━ [37m━━━━━━━━━━━ 3:27 250ms/步 - 损失: 1.0357 - 平均绝对误差: 0.8145

771/1599 ━━━━━━━━━ [37m━━━━━━━━━━━ 3:26 250ms/步 - 损失: 1.0357 - 平均绝对误差: 0.8144

772/1599 ━━━━━━━━━ [37m━━━━━━━━━━━ 3:26 250ms/步 - 损失: 1.0357 - 平均绝对误差: 0.8144

773/1599 ━━━━━━━━━ [37m━━━━━━━━━━━ 3:26 250ms/步 - 损失: 1.0357 - 平均绝对误差: 0.8144

774/1599 ━━━━━━━━━ [37m━━━━━━━━━━━ 3:26 250ms/步 - 损失: 1.0356 - 平均绝对误差: 0.8144

775/1599 ━━━━━━━━━ [37m━━━━━━━━━━━ 3:25 250ms/步 - 损失: 1.0356 - 平均绝对误差: 0.8144

776/1599 ━━━━━━━━━ [37m━━━━━━━━━━━ 3:25 250ms/步 - 损失: 1.0356 - 平均绝对误差: 0.8144

777/1599 ━━━━━━━━━ [37m━━━━━━━━━━━ 3:25 250ms/步 - 损失: 1.0356 - 平均绝对误差: 0.8144

778/1599 ━━━━━━━━━ [37m━━━━━━━━━━━ 3:25 250ms/步 - 损失: 1.0356 - 平均绝对误差: 0.8144

779/1599 ━━━━━━━━━ [37m━━━━━━━━━━━ 3:24 250ms/步 - 损失: 1.0355 - 平均绝对误差: 0.8144

780/1599 ━━━━━━━━━ [37m━━━━━━━━━━━ 3:24 250ms/步 - 损失: 1.0355 - 平均绝对误差: 0.8144

781/1599 ━━━━━━━━━ [37m━━━━━━━━━━━ 3:24 250ms/步 - 损失: 1.0355 - 平均绝对误差: 0.8144

782/1599 ━━━━━━━━━ [37m━━━━━━━━━━━ 3:24 250ms/步 - 损失: 1.0355 - 平均绝对误差: 0.8143

783/1599 ━━━━━━━━━ [37m━━━━━━━━━━━ 3:24 250ms/步 - 损失: 1.0355 - 平均绝对误差: 0.8143

784/1599 ━━━━━━━━━ [37m━━━━━━━━━━━ 3:23 250ms/步 - 损失: 1.0354 - 平均绝对误差: 0.8143

785/1599 ━━━━━━━━━ [37m━━━━━━━━━━━ 3:23 250ms/步 - 损失: 1.0354 - 平均绝对误差: 0.8143

786/1599 ━━━━━━━━━ [37m━━━━━━━━━━━ 3:23 251ms/步 - 损失: 1.0354 - 平均绝对误差: 0.8143

787/1599 ━━━━━━━━━ [37m━━━━━━━━━━━ 3:23 251ms/步 - 损失: 1.0354 - 平均绝对误差: 0.8143

788/1599 ━━━━━━━━━ [37m━━━━━━━━━━━ 3:23 251ms/步 - 损失: 1.0354 - 平均绝对误差: 0.8143

789/1599 ━━━━━━━━━ [37m━━━━━━━━━━━ 3:23 251ms/步 - 损失: 1.0353 - 平均绝对误差: 0.8143

790/1599 ━━━━━━━━━ [37m━━━━━━━━━━━ 3:23 251ms/步 - 损失: 1.0353 - 平均绝对误差: 0.8143

791/1599 ━━━━━━━━━ [37m━━━━━━━━━━━ 3:22 251ms/步 - 损失: 1.0353 - 平均绝对误差: 0.8143

792/1599 ━━━━━━━━━ [37m━━━━━━━━━━━ 3:22 251ms/步 - 损失: 1.0353 - 平均绝对误差: 0.8143

793/1599 ━━━━━━━━━ [37m━━━━━━━━━━━ 3:22 251ms/步 - 损失: 1.0352 - 平均绝对误差: 0.8142

794/1599 ━━━━━━━━━ [37m━━━━━━━━━━━ 3:22 251ms/步 - 损失: 1.0352 - 平均绝对误差: 0.8142

795/1599 ━━━━━━━━━ [37m━━━━━━━━━━━ 3:22 251ms/步 - 损失: 1.0352 - 平均绝对误差: 0.8142

796/1599 ━━━━━━━━━ [37m━━━━━━━━━━━ 3:21 251ms/步 - 损失: 1.0352 - 平均绝对误差: 0.8142

797/1599 ━━━━━━━━━ [37m━━━━━━━━━━━ 3:21 251ms/步 - 损失: 1.0352 - 平均绝对误差: 0.8142

798/1599 ━━━━━━━━━ [37m━━━━━━━━━━━ 3:21 251ms/步 - 损失: 1.0351 - 平均绝对误差: 0.8142

799/1599 ━━━━━━━━━ [37m━━━━━━━━━━━ 3:21 251ms/步 - 损失: 1.0351 - 平均绝对误差: 0.8142

800/1599 ━━━━━━━━━━ [37m━━━━━━━━━━ 3:20 251ms/步 - 损失: 1.0351 - 平均绝对误差: 0.8142

801/1599 ━━━━━━━━━━ [37m━━━━━━━━━━ 3:20 251ms/步 - 损失: 1.0351 - 平均绝对误差: 0.8142

802/1599 ━━━━━━━━━━ [37m━━━━━━━━━━ 3:20 251ms/步 - 损失: 1.0351 - 平均绝对误差: 0.8142

803/1599 ━━━━━━━━━━ [37m━━━━━━━━━━ 3:20 251ms/步 - 损失: 1.0351 - 平均绝对误差: 0.8142

804/1599 ━━━━━━━━━━ [37m━━━━━━━━━━ 3:19 251ms/步 - 损失: 1.0350 - 平均绝对误差: 0.8141

805/1599 ━━━━━━━━━━ [37m━━━━━━━━━━ 3:19 252ms/步 - 损失: 1.0350 - 平均绝对误差: 0.8141

806/1599 ━━━━━━━━━━ [37m━━━━━━━━━━ 3:19 252ms/步 - 损失: 1.0350 - 平均绝对误差: 0.8141

807/1599 ━━━━━━━━━━ [37m━━━━━━━━━━ 3:19 252ms/步 - 损失: 1.0350 - 平均绝对误差: 0.8141

808/1599 ━━━━━━━━━━ [37m━━━━━━━━━━ 3:19 252毫秒/步 - 损失: 1.0350 - 平均绝对误差: 0.8141

809/1599 ━━━━━━━━━━ [37m━━━━━━━━━━ 3:18 252毫秒/步 - 损失: 1.0349 - 平均绝对误差: 0.8141

810/1599 ━━━━━━━━━━ [37m━━━━━━━━━━ 3:18 252毫秒/步 - 损失: 1.0349 - 平均绝对误差: 0.8141

811/1599 ━━━━━━━━━━ [37m━━━━━━━━━━ 3:18 252毫秒/步 - 损失: 1.0349 - 平均绝对误差: 0.8141

812/1599 ━━━━━━━━━━ [37m━━━━━━━━━━ 3:18 252毫秒/步 - 损失: 1.0349 - 平均绝对误差: 0.8141

813/1599 ━━━━━━━━━━ [37m━━━━━━━━━━ 3:17 252毫秒/步 - 损失: 1.0349 - 平均绝对误差: 0.8141

814/1599 ━━━━━━━━━━ [37m━━━━━━━━━━ 3:17 252毫秒/步 - 损失: 1.0348 - 平均绝对误差: 0.8141

815/1599 ━━━━━━━━━━ [37m━━━━━━━━━━ 3:17 252毫秒/步 - 损失: 1.0348 - 平均绝对误差: 0.8141

816/1599 ━━━━━━━━━━ [37m━━━━━━━━━━ 3:17 252毫秒/步 - 损失: 1.0348 - 平均绝对误差: 0.8140

817/1599 ━━━━━━━━━━ [37m━━━━━━━━━━ 3:17 252毫秒/步 - 损失: 1.0348 - 平均绝对误差: 0.8140

818/1599 ━━━━━━━━━━ [37m━━━━━━━━━━ 3:17 252毫秒/步 - 损失: 1.0348 - 平均绝对误差: 0.8140

819/1599 ━━━━━━━━━━ [37m━━━━━━━━━━ 3:16 252毫秒/步 - 损失: 1.0347 - 平均绝对误差: 0.8140

820/1599 ━━━━━━━━━━ [37m━━━━━━━━━━ 3:16 253毫秒/步 - 损失: 1.0347 - 平均绝对误差: 0.8140

821/1599 ━━━━━━━━━━ [37m━━━━━━━━━━ 3:16 253毫秒/步 - 损失: 1.0347 - 平均绝对误差: 0.8140

822/1599 ━━━━━━━━━━ [37m━━━━━━━━━━ 3:16 253毫秒/步 - 损失: 1.0347 - 平均绝对误差: 0.8140

823/1599 ━━━━━━━━━━ [37m━━━━━━━━━━ 3:16 253毫秒/步 - 损失: 1.0347 - 平均绝对误差: 0.8140

824/1599 ━━━━━━━━━━ [37m━━━━━━━━━━ 3:15 253毫秒/步 - 损失: 1.0346 - 平均绝对误差: 0.8140

825/1599 ━━━━━━━━━━ [37m━━━━━━━━━━ 3:15 253毫秒/步 - 损失: 1.0346 - 平均绝对误差: 0.8140

826/1599 ━━━━━━━━━━ [37m━━━━━━━━━━ 3:15 253毫秒/步 - 损失: 1.0346 - 平均绝对误差: 0.8140

827/1599 ━━━━━━━━━━ [37m━━━━━━━━━━ 3:15 253毫秒/步 - 损失: 1.0346 - 平均绝对误差: 0.8140

828/1599 ━━━━━━━━━━ [37m━━━━━━━━━━ 3:15 253毫秒/步 - 损失: 1.0346 - 平均绝对误差: 0.8139

829/1599 ━━━━━━━━━━ [37m━━━━━━━━━━ 3:14 253毫秒/步 - 损失: 1.0346 - 平均绝对误差: 0.8139

830/1599 ━━━━━━━━━━ [37m━━━━━━━━━━ 3:14 253毫秒/步 - 损失: 1.0345 - 平均绝对误差: 0.8139

831/1599 ━━━━━━━━━━ [37m━━━━━━━━━━ 3:14 253毫秒/步 - 损失: 1.0345 - 平均绝对误差: 0.8139

832/1599 ━━━━━━━━━━ [37m━━━━━━━━━━ 3:14 253毫秒/步 - 损失: 1.0345 - 平均绝对误差: 0.8139

833/1599 ━━━━━━━━━━ [37m━━━━━━━━━━ 3:13 253毫秒/步 - 损失: 1.0345 - 平均绝对误差: 0.8139

834/1599 ━━━━━━━━━━ [37m━━━━━━━━━━ 3:13 253毫秒/步 - 损失: 1.0345 - 平均绝对误差: 0.8139

835/1599 ━━━━━━━━━━ [37m━━━━━━━━━━ 3:13 253毫秒/步 - 损失: 1.0344 - 平均绝对误差: 0.8139

836/1599 ━━━━━━━━━━ [37m━━━━━━━━━━ 3:13 253毫秒/步 - 损失: 1.0344 - 平均绝对误差: 0.8139

837/1599 ━━━━━━━━━━ [37m━━━━━━━━━━ 3:12 253毫秒/步 - 损失: 1.0344 - 平均绝对误差: 0.8139

838/1599 ━━━━━━━━━━ [37m━━━━━━━━━━ 3:12 253毫秒/步 - 损失: 1.0344 - 平均绝对误差: 0.8139

839/1599 ━━━━━━━━━━ [37m━━━━━━━━━━ 3:12 253毫秒/步 - 损失: 1.0344 - 平均绝对误差: 0.8139

840/1599 ━━━━━━━━━━ [37m━━━━━━━━━━ 3:12 253毫秒/步 - 损失: 1.0344 - 平均绝对误差: 0.8138

841/1599 ━━━━━━━━━━ [37m━━━━━━━━━━ 3:11 253毫秒/步 - 损失: 1.0343 - 平均绝对误差: 0.8138

842/1599 ━━━━━━━━━━ [37m━━━━━━━━━━ 3:11 253毫秒/步 - 损失: 1.0343 - 平均绝对误差: 0.8138

843/1599 ━━━━━━━━━━ [37m━━━━━━━━━━ 3:11 253毫秒/步 - 损失: 1.0343 - 平均绝对误差: 0.8138

844/1599 ━━━━━━━━━━ [37m━━━━━━━━━━ 3:11 253毫秒/步 - 损失: 1.0343 - 平均绝对误差: 0.8138

845/1599 ━━━━━━━━━━ [37m━━━━━━━━━━ 3:10 253毫秒/步 - 损失: 1.0343 - 平均绝对误差: 0.8138

846/1599 ━━━━━━━━━━ [37m━━━━━━━━━━ 3:10 253毫秒/步 - 损失: 1.0343 - 平均绝对误差: 0.8138

847/1599 ━━━━━━━━━━ [37m━━━━━━━━━━ 3:10 253毫秒/步 - 损失: 1.0342 - 平均绝对误差: 0.8138

848/1599 ━━━━━━━━━━ [37m━━━━━━━━━━ 3:10 253毫秒/步 - 损失: 1.0342 - 平均绝对误差: 0.8138

849/1599 ━━━━━━━━━━ [37m━━━━━━━━━━ 3:09 253毫秒/步 - 损失: 1.0342 - 平均绝对误差: 0.8138

850/1599 ━━━━━━━━━━ [37m━━━━━━━━━━ 3:09 253毫秒/步 - 损失: 1.0342 - 平均绝对误差: 0.8138

851/1599 ━━━━━━━━━━ [37m━━━━━━━━━━ 3:09 253毫秒/步 - 损失: 1.0342 - 平均绝对误差: 0.8138

852/1599 ━━━━━━━━━━ [37m━━━━━━━━━━ 3:09 253毫秒/步 - 损失: 1.0341 - 平均绝对误差: 0.8138

853/1599 ━━━━━━━━━━ [37m━━━━━━━━━━ 3:08 253毫秒/步 - 损失: 1.0341 - 平均绝对误差: 0.8137

854/1599 ━━━━━━━━━━ [37m━━━━━━━━━━ 3:08 253毫秒/步 - 损失: 1.0341 - 平均绝对误差: 0.8137

855/1599 ━━━━━━━━━━ [37m━━━━━━━━━━ 3:08 253毫秒/步 - 损失: 1.0341 - 平均绝对误差: 0.8137

856/1599 ━━━━━━━━━━ [37m━━━━━━━━━━ 3:08 253毫秒/步 - 损失: 1.0341 - 平均绝对误差: 0.8137

857/1599 ━━━━━━━━━━ [37m━━━━━━━━━━ 3:07 253毫秒/步 - 损失: 1.0341 - 平均绝对误差: 0.8137

858/1599 ━━━━━━━━━━ [37m━━━━━━━━━━ 3:07 253毫秒/步 - 损失: 1.0340 - 平均绝对误差: 0.8137

859/1599 ━━━━━━━━━━ [37m━━━━━━━━━━ 3:07 253毫秒/步 - 损失: 1.0340 - 平均绝对误差: 0.8137

860/1599 ━━━━━━━━━━ [37m━━━━━━━━━━ 3:07 253毫秒/步 - 损失: 1.0340 - 平均绝对误差: 0.8137

861/1599 ━━━━━━━━━━ [37m━━━━━━━━━━ 3:06 253毫秒/步 - 损失: 1.0340 - 平均绝对误差: 0.8137

862/1599 ━━━━━━━━━━ [37m━━━━━━━━━━ 3:06 253毫秒/步 - 损失: 1.0340 - 平均绝对误差: 0.8137

863/1599 ━━━━━━━━━━ [37m━━━━━━━━━━ 3:06 253毫秒/步 - 损失: 1.0340 - 平均绝对误差: 0.8137

864/1599 ━━━━━━━━━━ [37m━━━━━━━━━━ 3:06 253毫秒/步 - 损失: 1.0339 - 平均绝对误差: 0.8137

865/1599 ━━━━━━━━━━ [37m━━━━━━━━━━ 3:05 253毫秒/步 - 损失: 1.0339 - 平均绝对误差: 0.8137

866/1599 ━━━━━━━━━━ [37m━━━━━━━━━━ 3:05 253毫秒/步 - 损失: 1.0339 - 平均绝对误差: 0.8136

867/1599 ━━━━━━━━━━ [37m━━━━━━━━━━ 3:05 253毫秒/步 - 损失: 1.0339 - 平均绝对误差: 0.8136

868/1599 ━━━━━━━━━━ [37m━━━━━━━━━━ 3:05 253毫秒/步 - 损失: 1.0339 - 平均绝对误差: 0.8136

869/1599 ━━━━━━━━━━ [37m━━━━━━━━━━ 3:04 253毫秒/步 - 损失: 1.0338 - 平均绝对误差: 0.8136

870/1599 ━━━━━━━━━━ [37m━━━━━━━━━━ 3:04 253毫秒/步 - 损失: 1.0338 - 平均绝对误差: 0.8136

871/1599 ━━━━━━━━━━ [37m━━━━━━━━━━ 3:04 253毫秒/步 - 损失: 1.0338 - 平均绝对误差: 0.8136

872/1599 ━━━━━━━━━━ [37m━━━━━━━━━━ 3:04 253毫秒/步 - 损失: 1.0338 - 平均绝对误差: 0.8136

873/1599 ━━━━━━━━━━ [37m━━━━━━━━━━ 3:03 253毫秒/步 - 损失: 1.0338 - 平均绝对误差: 0.8136

874/1599 ━━━━━━━━━━ [37m━━━━━━━━━━ 3:03 253毫秒/步 - 损失: 1.0338 - 平均绝对误差: 0.8136

875/1599 ━━━━━━━━━━ [37m━━━━━━━━━━ 3:03 253毫秒/步 - 损失: 1.0337 - 平均绝对误差: 0.8136

876/1599 ━━━━━━━━━━ [37m━━━━━━━━━━ 3:03 253毫秒/步 - 损失: 1.0337 - 平均绝对误差: 0.8136

877/1599 ━━━━━━━━━━ [37m━━━━━━━━━━ 3:02 253毫秒/步 - 损失: 1.0337 - 平均绝对误差: 0.8136

878/1599 ━━━━━━━━━━ [37m━━━━━━━━━━ 3:02 253毫秒/步 - 损失: 1.0337 - 平均绝对误差: 0.8136

879/1599 ━━━━━━━━━━ [37m━━━━━━━━━━ 3:02 253毫秒/步 - 损失: 1.0337 - 平均绝对误差: 0.8135

880/1599 ━━━━━━━━━━━ [37m━━━━━━━━━ 3:02 253毫秒/步 - 损失: 1.0337 - 平均绝对误差: 0.8135

881/1599 ━━━━━━━━━━━ [37m━━━━━━━━━ 3:01 253毫秒/步 - 损失: 1.0336 - 平均绝对误差: 0.8135

882/1599 ━━━━━━━━━━━ [37m━━━━━━━━━ 3:01 253毫秒/步 - 损失: 1.0336 - 平均绝对误差: 0.8135

883/1599 ━━━━━━━━━━━ [37m━━━━━━━━━ 3:01 253毫秒/步 - 损失: 1.0336 - 平均绝对误差: 0.8135

884/1599 ━━━━━━━━━━━ [37m━━━━━━━━━ 3:01 253毫秒/步 - 损失: 1.0336 - 平均绝对误差: 0.8135

885/1599 ━━━━━━━━━━━ [37m━━━━━━━━━ 3:00 253毫秒/步 - 损失: 1.0336 - 平均绝对误差: 0.8135

886/1599 ━━━━━━━━━━━ [37m━━━━━━━━━ 3:00 253毫秒/步 - 损失: 1.0336 - 平均绝对误差: 0.8135

887/1599 ━━━━━━━━━━━ [37m━━━━━━━━━ 3:00 253毫秒/步 - 损失: 1.0335 - 平均绝对误差: 0.8135

888/1599 ━━━━━━━━━━━ [37m━━━━━━━━━ 3:00 253毫秒/步 - 损失: 1.0335 - 平均绝对误差: 0.8135

889/1599 ━━━━━━━━━━━ [37m━━━━━━━━━ 2:59 253毫秒/步 - 损失: 1.0335 - 平均绝对误差: 0.8135

890/1599 ━━━━━━━━━━━ [37m━━━━━━━━━ 2:59 253毫秒/步 - 损失: 1.0335 - 平均绝对误差: 0.8135

891/1599 ━━━━━━━━━━━ [37m━━━━━━━━━ 2:59 253毫秒/步 - 损失: 1.0335 - 平均绝对误差: 0.8135

892/1599 ━━━━━━━━━━━ [37m━━━━━━━━━ 2:59 253毫秒/步 - 损失: 1.0335 - 平均绝对误差: 0.8135

893/1599 ━━━━━━━━━━━ [37m━━━━━━━━━ 2:58 253毫秒/步 - 损失: 1.0334 - 平均绝对误差: 0.8134

894/1599 ━━━━━━━━━━━ [37m━━━━━━━━━ 2:58 253毫秒/步 - 损失: 1.0334 - 平均绝对误差: 0.8134

895/1599 ━━━━━━━━━━━ [37m━━━━━━━━━ 2:58 253毫秒/步 - 损失: 1.0334 - 平均绝对误差: 0.8134

896/1599 ━━━━━━━━━━━ [37m━━━━━━━━━ 2:58 253毫秒/步 - 损失: 1.0334 - 平均绝对误差: 0.8134

897/1599 ━━━━━━━━━━━ [37m━━━━━━━━━ 2:57 253毫秒/步 - 损失: 1.0334 - 平均绝对误差: 0.8134

898/1599 ━━━━━━━━━━━ [37m━━━━━━━━━ 2:57 253毫秒/步 - 损失: 1.0334 - 平均绝对误差: 0.8134

899/1599 ━━━━━━━━━━━ [37m━━━━━━━━━ 2:57 253毫秒/步 - 损失: 1.0334 - 平均绝对误差: 0.8134

900/1599 ━━━━━━━━━━━ [37m━━━━━━━━━ 2:57 253毫秒/步 - 损失: 1.0333 - 平均绝对误差: 0.8134

901/1599 ━━━━━━━━━━━ [37m━━━━━━━━━ 2:56 253毫秒/步 - 损失: 1.0333 - 平均绝对误差: 0.8134

902/1599 ━━━━━━━━━━━ [37m━━━━━━━━━ 2:56 253毫秒/步 - 损失: 1.0333 - 平均绝对误差: 0.8134

903/1599 ━━━━━━━━━━━ [37m━━━━━━━━━ 2:56 253毫秒/步 - 损失: 1.0333 - 平均绝对误差: 0.8134

904/1599 ━━━━━━━━━━━ [37m━━━━━━━━━ 2:56 253毫秒/步 - 损失: 1.0333 - 平均绝对误差: 0.8134

905/1599 ━━━━━━━━━━━ [37m━━━━━━━━━ 2:55 253毫秒/步 - 损失: 1.0333 - 平均绝对误差: 0.8134

906/1599 ━━━━━━━━━━━ [37m━━━━━━━━━ 2:55 253毫秒/步 - 损失: 1.0332 - 平均绝对误差: 0.8134

907/1599 ━━━━━━━━━━━ [37m━━━━━━━━━ 2:55 253毫秒/步 - 损失: 1.0332 - 平均绝对误差: 0.8134

908/1599 ━━━━━━━━━━━ [37m━━━━━━━━━ 2:55 253毫秒/步 - 损失: 1.0332 - 平均绝对误差: 0.8133

909/1599 ━━━━━━━━━━━ [37m━━━━━━━━━ 2:54 254毫秒/步 - 损失: 1.0332 - 平均绝对误差: 0.8133

910/1599 ━━━━━━━━━━━ [37m━━━━━━━━━ 2:54 254毫秒/步 - 损失: 1.0332 - 平均绝对误差: 0.8133

911/1599 ━━━━━━━━━━━ [37m━━━━━━━━━ 2:54 254毫秒/步 - 损失: 1.0332 - 平均绝对误差: 0.8133

912/1599 ━━━━━━━━━━━ [37m━━━━━━━━━ 2:54 253毫秒/步 - 损失: 1.0332 - 平均绝对误差: 0.8133

913/1599 ━━━━━━━━━━━ [37m━━━━━━━━━ 2:53 253毫秒/步 - 损失: 1.0331 - 平均绝对误差: 0.8133

914/1599 ━━━━━━━━━━━ [37m━━━━━━━━━ 2:53 254毫秒/步 - 损失: 1.0331 - 平均绝对误差: 0.8133

915/1599 ━━━━━━━━━━━ [37m━━━━━━━━━ 2:53 254毫秒/步 - 损失: 1.0331 - 平均绝对误差: 0.8133

916/1599 ━━━━━━━━━━━ [37m━━━━━━━━━ 2:53 254毫秒/步 - 损失: 1.0331 - 平均绝对误差: 0.8133

917/1599 ━━━━━━━━━━━ [37m━━━━━━━━━ 2:52 254毫秒/步 - 损失: 1.0331 - 平均绝对误差: 0.8133

918/1599 ━━━━━━━━━━━ [37m━━━━━━━━━ 2:52 254毫秒/步 - 损失: 1.0331 - 平均绝对误差: 0.8133

919/1599 ━━━━━━━━━━━ [37m━━━━━━━━━ 2:52 254毫秒/步 - 损失: 1.0330 - 平均绝对误差: 0.8133

920/1599 ━━━━━━━━━━━ [37m━━━━━━━━━ 2:52 254毫秒/步 - 损失: 1.0330 - 平均绝对误差: 0.8133

921/1599 ━━━━━━━━━━━ [37m━━━━━━━━━ 2:51 254毫秒/步 - 损失: 1.0330 - 平均绝对误差: 0.8133

922/1599 ━━━━━━━━━━━ [37m━━━━━━━━━ 2:51 254毫秒/步 - 损失: 1.0330 - 平均绝对误差: 0.8132

923/1599 ━━━━━━━━━━━ [37m━━━━━━━━━ 2:51 254毫秒/步 - 损失: 1.0330 - 平均绝对误差: 0.8132

924/1599 ━━━━━━━━━━━ [37m━━━━━━━━━ 2:51 254毫秒/步 - 损失: 1.0330 - 平均绝对误差: 0.8132

925/1599 ━━━━━━━━━━━ [37m━━━━━━━━━ 2:50 254毫秒/步 - 损失: 1.0329 - 平均绝对误差: 0.8132

926/1599 ━━━━━━━━━━━ [37m━━━━━━━━━ 2:50 254毫秒/步 - 损失: 1.0329 - 平均绝对误差: 0.8132

927/1599 ━━━━━━━━━━━ [37m━━━━━━━━━ 2:50 254毫秒/步 - 损失: 1.0329 - 平均绝对误差: 0.8132

928/1599 ━━━━━━━━━━━ [37m━━━━━━━━━ 2:50 254毫秒/步 - 损失: 1.0329 - 平均绝对误差: 0.8132

929/1599 ━━━━━━━━━━━ [37m━━━━━━━━━ 2:50 254毫秒/步 - 损失: 1.0329 - 平均绝对误差: 0.8132

930/1599 ━━━━━━━━━━━ [37m━━━━━━━━━ 2:49 254毫秒/步 - 损失: 1.0329 - 平均绝对误差: 0.8132

931/1599 ━━━━━━━━━━━ [37m━━━━━━━━━ 2:49 254毫秒/步 - 损失: 1.0329 - 平均绝对误差: 0.8132

932/1599 ━━━━━━━━━━━ [37m━━━━━━━━━ 2:49 254毫秒/步 - 损失: 1.0328 - 平均绝对误差: 0.8132

933/1599 ━━━━━━━━━━━ [37m━━━━━━━━━ 2:49 254毫秒/步 - 损失: 1.0328 - 平均绝对误差: 0.8132

934/1599 ━━━━━━━━━━━ [37m━━━━━━━━━ 2:48 254毫秒/步 - 损失: 1.0328 - 平均绝对误差: 0.8132

935/1599 ━━━━━━━━━━━ [37m━━━━━━━━━ 2:48 254毫秒/步 - 损失: 1.0328 - 平均绝对误差: 0.8131

936/1599 ━━━━━━━━━━━ [37m━━━━━━━━━ 2:48 254毫秒/步 - 损失: 1.0328 - 平均绝对误差: 0.8131

937/1599 ━━━━━━━━━━━ [37m━━━━━━━━━ 2:48 254毫秒/步 - 损失: 1.0327 - 平均绝对误差: 0.8131

938/1599 ━━━━━━━━━━━ [37m━━━━━━━━━ 2:47 254毫秒/步 - 损失: 1.0327 - 平均绝对误差: 0.8131

939/1599 ━━━━━━━━━━━ [37m━━━━━━━━━ 2:47 254毫秒/步 - 损失: 1.0327 - 平均绝对误差: 0.8131

940/1599 ━━━━━━━━━━━ [37m━━━━━━━━━ 2:47 254毫秒/步 - 损失: 1.0327 - 平均绝对误差: 0.8131

941/1599 ━━━━━━━━━━━ [37m━━━━━━━━━ 2:46 254毫秒/步 - 损失: 1.0327 - 平均绝对误差: 0.8131

942/1599 ━━━━━━━━━━━ [37m━━━━━━━━━ 2:46 254毫秒/步 - 损失: 1.0327 - 平均绝对误差: 0.8131

943/1599 ━━━━━━━━━━━ [37m━━━━━━━━━ 2:46 254毫秒/步 - 损失: 1.0326 - 平均绝对误差: 0.8131

944/1599 ━━━━━━━━━━━ [37m━━━━━━━━━ 2:46 254毫秒/步 - 损失: 1.0326 - 平均绝对误差: 0.8131

945/1599 ━━━━━━━━━━━ [37m━━━━━━━━━ 2:45 254毫秒/步 - 损失: 1.0326 - 平均绝对误差: 0.8131

946/1599 ━━━━━━━━━━━ [37m━━━━━━━━━ 2:45 254毫秒/步 - 损失: 1.0326 - 平均绝对误差: 0.8131

947/1599 ━━━━━━━━━━━ [37m━━━━━━━━━ 2:45 254毫秒/步 - 损失: 1.0326 - 平均绝对误差: 0.8131

948/1599 ━━━━━━━━━━━ [37m━━━━━━━━━ 2:45 254毫秒/步 - 损失: 1.0326 - 平均绝对误差: 0.8131

949/1599 ━━━━━━━━━━━ [37m━━━━━━━━━ 2:44 254毫秒/步 - 损失: 1.0325 - 平均绝对误差: 0.8130

950/1599 ━━━━━━━━━━━ [37m━━━━━━━━━ 2:44 254毫秒/步 - 损失: 1.0325 - 平均绝对误差: 0.8130

951/1599 ━━━━━━━━━━━ [37m━━━━━━━━━ 2:44 254毫秒/步 - 损失: 1.0325 - 平均绝对误差: 0.8130

952/1599 ━━━━━━━━━━━ [37m━━━━━━━━━ 2:44 254毫秒/步 - 损失: 1.0325 - 平均绝对误差: 0.8130

953/1599 ━━━━━━━━━━━ [37m━━━━━━━━━ 2:43 254毫秒/步 - 损失: 1.0325 - 平均绝对误差: 0.8130

954/1599 ━━━━━━━━━━━ [37m━━━━━━━━━ 2:43 254毫秒/步 - 损失: 1.0325 - 平均绝对误差: 0.8130

955/1599 ━━━━━━━━━━━ [37m━━━━━━━━━ 2:43 254毫秒/步 - 损失: 1.0324 - 平均绝对误差: 0.8130

956/1599 ━━━━━━━━━━━ [37m━━━━━━━━━ 2:43 254毫秒/步 - 损失: 1.0324 - 平均绝对误差: 0.8130

957/1599 ━━━━━━━━━━━ [37m━━━━━━━━━ 2:42 254毫秒/步 - 损失: 1.0324 - 平均绝对误差: 0.8130

958/1599 ━━━━━━━━━━━ [37m━━━━━━━━━ 2:42 254毫秒/步 - 损失: 1.0324 - 平均绝对误差: 0.8130

959/1599 ━━━━━━━━━━━ [37m━━━━━━━━━ 2:42 254毫秒/步 - 损失: 1.0324 - 平均绝对误差: 0.8130

960/1599 ━━━━━━━━━━━━ [37m━━━━━━━━ 2:42 254毫秒/步 - 损失: 1.0323 - 平均绝对误差: 0.8130

961/1599 ━━━━━━━━━━━━ [37m━━━━━━━━ 2:41 254毫秒/步 - 损失: 1.0323 - 平均绝对误差: 0.8129

962/1599 ━━━━━━━━━━━━ [37m━━━━━━━━ 2:41 254毫秒/步 - 损失: 1.0323 - 平均绝对误差: 0.8129

963/1599 ━━━━━━━━━━━━ [37m━━━━━━━━ 2:41 254毫秒/步 - 损失: 1.0323 - 平均绝对误差: 0.8129

964/1599 ━━━━━━━━━━━━ [37m━━━━━━━━ 2:41 254毫秒/步 - 损失: 1.0323 - 平均绝对误差: 0.8129

965/1599 ━━━━━━━━━━━━ [37m━━━━━━━━ 2:40 254毫秒/步 - 损失: 1.0322 - 平均绝对误差: 0.8129

966/1599 ━━━━━━━━━━━━ [37m━━━━━━━━ 2:40 254毫秒/步 - 损失: 1.0322 - 平均绝对误差: 0.8129

967/1599 ━━━━━━━━━━━━ [37m━━━━━━━━ 2:40 254毫秒/步 - 损失: 1.0322 - 平均绝对误差: 0.8129

968/1599 ━━━━━━━━━━━━ [37m━━━━━━━━ 2:40 254毫秒/步 - 损失: 1.0322 - 平均绝对误差: 0.8129

969/1599 ━━━━━━━━━━━━ [37m━━━━━━━━ 2:39 254毫秒/步 - 损失: 1.0322 - 平均绝对误差: 0.8129

970/1599 ━━━━━━━━━━━━ [37m━━━━━━━━ 2:39 254毫秒/步 - 损失: 1.0322 - 平均绝对误差: 0.8129

971/1599 ━━━━━━━━━━━━ [37m━━━━━━━━ 2:39 254毫秒/步 - 损失: 1.0321 - 平均绝对误差: 0.8129

972/1599 ━━━━━━━━━━━━ [37m━━━━━━━━ 2:39 254毫秒/步 - 损失: 1.0321 - 平均绝对误差: 0.8129

973/1599 ━━━━━━━━━━━━ [37m━━━━━━━━ 2:38 254毫秒/步 - 损失: 1.0321 - 平均绝对误差: 0.8128

974/1599 ━━━━━━━━━━━━ [37m━━━━━━━━ 2:38 254毫秒/步 - 损失: 1.0321 - 平均绝对误差: 0.8128

975/1599 ━━━━━━━━━━━━ [37m━━━━━━━━ 2:38 254毫秒/步 - 损失: 1.0321 - 平均绝对误差: 0.8128

976/1599 ━━━━━━━━━━━━ [37m━━━━━━━━ 2:38 254毫秒/步 - 损失: 1.0320 - 平均绝对误差: 0.8128

977/1599 ━━━━━━━━━━━━ [37m━━━━━━━━ 2:37 254毫秒/步 - 损失: 1.0320 - 平均绝对误差: 0.8128

978/1599 ━━━━━━━━━━━━ [37m━━━━━━━━ 2:37 254毫秒/步 - 损失: 1.0320 - 平均绝对误差: 0.8128

979/1599 ━━━━━━━━━━━━ [37m━━━━━━━━ 2:37 254毫秒/步 - 损失: 1.0320 - 平均绝对误差: 0.8128

980/1599 ━━━━━━━━━━━━ [37m━━━━━━━━ 2:37 254毫秒/步 - 损失: 1.0320 - 平均绝对误差: 0.8128

981/1599 ━━━━━━━━━━━━ [37m━━━━━━━━ 2:36 254毫秒/步 - 损失: 1.0319 - 平均绝对误差: 0.8128

982/1599 ━━━━━━━━━━━━ [37m━━━━━━━━ 2:36 254毫秒/步 - 损失: 1.0319 - 平均绝对误差: 0.8128

983/1599 ━━━━━━━━━━━━ [37m━━━━━━━━ 2:36 254毫秒/步 - 损失: 1.0319 - 平均绝对误差: 0.8128

984/1599 ━━━━━━━━━━━━ [37m━━━━━━━━ 2:36 254毫秒/步 - 损失: 1.0319 - 平均绝对误差: 0.8128

985/1599 ━━━━━━━━━━━━ [37m━━━━━━━━ 2:35 254毫秒/步 - 损失: 1.0319 - 平均绝对误差: 0.8127

986/1599 ━━━━━━━━━━━━ [37m━━━━━━━━ 2:35 254毫秒/步 - 损失: 1.0319 - 平均绝对误差: 0.8127

987/1599 ━━━━━━━━━━━━ [37m━━━━━━━━ 2:35 254毫秒/步 - 损失: 1.0318 - 平均绝对误差: 0.8127

988/1599 ━━━━━━━━━━━━ [37m━━━━━━━━ 2:35 254毫秒/步 - 损失: 1.0318 - 平均绝对误差: 0.8127

989/1599 ━━━━━━━━━━━━ [37m━━━━━━━━ 2:35 254毫秒/步 - 损失: 1.0318 - 平均绝对误差: 0.8127

990/1599 ━━━━━━━━━━━━ [37m━━━━━━━━ 2:34 254毫秒/步 - 损失: 1.0318 - 平均绝对误差: 0.8127

991/1599 ━━━━━━━━━━━━ [37m━━━━━━━━ 2:34 254毫秒/步 - 损失: 1.0318 - 平均绝对误差: 0.8127

992/1599 ━━━━━━━━━━━━ [37m━━━━━━━━ 2:34 254毫秒/步 - 损失: 1.0317 - 平均绝对误差: 0.8127

993/1599 ━━━━━━━━━━━━ [37m━━━━━━━━ 2:34 254毫秒/步 - 损失: 1.0317 - 平均绝对误差: 0.8127

994/1599 ━━━━━━━━━━━━ [37m━━━━━━━━ 2:33 254毫秒/步 - 损失: 1.0317 - 平均绝对误差: 0.8127

995/1599 ━━━━━━━━━━━━ [37m━━━━━━━━ 2:33 254毫秒/步 - 损失: 1.0317 - 平均绝对误差: 0.8127

996/1599 ━━━━━━━━━━━━ [37m━━━━━━━━ 2:33 254毫秒/步 - 损失: 1.0317 - 平均绝对误差: 0.8126

997/1599 ━━━━━━━━━━━━ [37m━━━━━━━━ 2:33 254毫秒/步 - 损失: 1.0316 - 平均绝对误差: 0.8126

998/1599 ━━━━━━━━━━━━ [37m━━━━━━━━ 2:32 254毫秒/步 - 损失: 1.0316 - 平均绝对误差: 0.8126

999/1599 ━━━━━━━━━━━━ [37m━━━━━━━━ 2:32 254毫秒/步 - 损失: 1.0316 - 平均绝对误差: 0.8126

1000/1599 ━━━━━━━━━━━━ [37m━━━━━━━━ 2:32 254毫秒/步 - 损失: 1.0316 - 平均绝对误差: 0.8126

1001/1599 ━━━━━━━━━━━━ [37m━━━━━━━━ 2:32 255毫秒/步 - 损失: 1.0316 - 平均绝对误差: 0.8126

1002/1599 ━━━━━━━━━━━━ [37m━━━━━━━━ 2:31 255毫秒/步 - 损失: 1.0316 - 平均绝对误差: 0.8126

1003/1599 ━━━━━━━━━━━━ [37m━━━━━━━━ 2:31 255毫秒/步 - 损失: 1.0315 - 平均绝对误差: 0.8126

1004/1599 ━━━━━━━━━━━━ [37m━━━━━━━━ 2:31 255毫秒/步 - 损失: 1.0315 - 平均绝对误差: 0.8126

1005/1599 ━━━━━━━━━━━━ [37m━━━━━━━━ 2:31 255毫秒/步 - 损失: 1.0315 - 平均绝对误差: 0.8126

1006/1599 ━━━━━━━━━━━━ [37m━━━━━━━━ 2:30 255毫秒/步 - 损失: 1.0315 - 平均绝对误差: 0.8126

1007/1599 ━━━━━━━━━━━━ [37m━━━━━━━━ 2:30 255毫秒/步 - 损失: 1.0315 - 平均绝对误差: 0.8126

1008/1599 ━━━━━━━━━━━━ [37m━━━━━━━━ 2:30 255毫秒/步 - 损失: 1.0315 - 平均绝对误差: 0.8126

1009/1599 ━━━━━━━━━━━━ [37m━━━━━━━━ 2:30 255毫秒/步 - 损失: 1.0314 - 平均绝对误差: 0.8125

1010/1599 ━━━━━━━━━━━━ [37m━━━━━━━━ 2:30 255毫秒/步 - 损失: 1.0314 - 平均绝对误差: 0.8125

1011/1599 ━━━━━━━━━━━━ [37m━━━━━━━━ 2:29 255毫秒/步 - 损失: 1.0314 - 平均绝对误差: 0.8125

1012/1599 ━━━━━━━━━━━━ [37m━━━━━━━━ 2:29 255毫秒/步 - 损失: 1.0314 - 平均绝对误差: 0.8125

1013/1599 ━━━━━━━━━━━━ [37m━━━━━━━━ 2:29 255毫秒/步 - 损失: 1.0314 - 平均绝对误差: 0.8125

1014/1599 ━━━━━━━━━━━━ [37m━━━━━━━━ 2:29 255毫秒/步 - 损失: 1.0313 - 平均绝对误差: 0.8125

1015/1599 ━━━━━━━━━━━━ [37m━━━━━━━━ 2:28 255毫秒/步 - 损失: 1.0313 - 平均绝对误差: 0.8125

1016/1599 ━━━━━━━━━━━━ [37m━━━━━━━━ 2:28 255毫秒/步 - 损失: 1.0313 - 平均绝对误差: 0.8125

1017/1599 ━━━━━━━━━━━━ [37m━━━━━━━━ 2:28 255毫秒/步 - 损失: 1.0313 - 平均绝对误差: 0.8125

1018/1599 ━━━━━━━━━━━━ [37m━━━━━━━━ 2:28 255毫秒/步 - 损失: 1.0313 - 平均绝对误差: 0.8125

1019/1599 ━━━━━━━━━━━━ [37m━━━━━━━━ 2:27 255毫秒/步 - 损失: 1.0313 - 平均绝对误差: 0.8125

1020/1599 ━━━━━━━━━━━━ [37m━━━━━━━━ 2:27 255毫秒/步 - 损失: 1.0312 - 平均绝对误差: 0.8125

1021/1599 ━━━━━━━━━━━━ [37m━━━━━━━━ 2:27 255毫秒/步 - 损失: 1.0312 - 平均绝对误差: 0.8124

1022/1599 ━━━━━━━━━━━━ [37m━━━━━━━━ 2:27 255毫秒/步 - 损失: 1.0312 - 平均绝对误差: 0.8124

1023/1599 ━━━━━━━━━━━━ [37m━━━━━━━━ 2:27 255毫秒/步 - 损失: 1.0312 - 平均绝对误差: 0.8124

1024/1599 ━━━━━━━━━━━━ [37m━━━━━━━━ 2:26 255毫秒/步 - 损失: 1.0312 - 平均绝对误差: 0.8124

1025/1599 ━━━━━━━━━━━━ [37m━━━━━━━━ 2:26 255毫秒/步 - 损失: 1.0311 - 平均绝对误差: 0.8124

1026/1599 ━━━━━━━━━━━━ [37m━━━━━━━━ 2:26 255毫秒/步 - 损失: 1.0311 - 平均绝对误差: 0.8124

1027/1599 ━━━━━━━━━━━━ [37m━━━━━━━━ 2:26 256毫秒/步 - 损失: 1.0311 - 平均绝对误差: 0.8124

1028/1599 ━━━━━━━━━━━━ [37m━━━━━━━━ 2:25 256毫秒/步 - 损失: 1.0311 - 平均绝对误差: 0.8124

1029/1599 ━━━━━━━━━━━━ [37m━━━━━━━━ 2:25 256毫秒/步 - 损失: 1.0311 - 平均绝对误差: 0.8124

1030/1599 ━━━━━━━━━━━━ [37m━━━━━━━━ 2:25 256毫秒/步 - 损失: 1.0310 - 平均绝对误差: 0.8124

1031/1599 ━━━━━━━━━━━━ [37m━━━━━━━━ 2:25 256毫秒/步 - 损失: 1.0310 - 平均绝对误差: 0.8124

1032/1599 ━━━━━━━━━━━━ [37m━━━━━━━━ 2:25 256毫秒/步 - 损失: 1.0310 - 平均绝对误差: 0.8124

1033/1599 ━━━━━━━━━━━━ [37m━━━━━━━━ 2:24 256毫秒/步 - 损失: 1.0310 - 平均绝对误差: 0.8123

1034/1599 ━━━━━━━━━━━━ [37m━━━━━━━━ 2:24 256毫秒/步 - 损失: 1.0310 - 平均绝对误差: 0.8123

1035/1599 ━━━━━━━━━━━━ [37m━━━━━━━━ 2:24 256毫秒/步 - 损失: 1.0310 - 平均绝对误差: 0.8123

1036/1599 ━━━━━━━━━━━━ [37m━━━━━━━━ 2:24 256毫秒/步 - 损失: 1.0309 - 平均绝对误差: 0.8123

1037/1599 ━━━━━━━━━━━━ [37m━━━━━━━━ 2:23 256毫秒/步 - 损失: 1.0309 - 平均绝对误差: 0.8123

1038/1599 ━━━━━━━━━━━━ [37m━━━━━━━━ 2:23 256毫秒/步 - 损失: 1.0309 - 平均绝对误差: 0.8123

1039/1599 ━━━━━━━━━━━━ [37m━━━━━━━━ 2:23 256毫秒/步 - 损失: 1.0309 - 平均绝对误差: 0.8123

1040/1599 ━━━━━━━━━━━━━ [37m━━━━━━━ 2:23 256毫秒/步 - 损失: 1.0309 - 平均绝对误差: 0.8123

1041/1599 ━━━━━━━━━━━━━ [37m━━━━━━━ 2:22 256毫秒/步 - 损失: 1.0308 - 平均绝对误差: 0.8123

1042/1599 ━━━━━━━━━━━━━ [37m━━━━━━━ 2:22 256毫秒/步 - 损失: 1.0308 - 平均绝对误差: 0.8123

1043/1599 ━━━━━━━━━━━━━ [37m━━━━━━━ 2:22 256毫秒/步 - 损失: 1.0308 - 平均绝对误差: 0.8123

1044/1599 ━━━━━━━━━━━━━ [37m━━━━━━━ 2:22 256毫秒/步 - 损失: 1.0308 - 平均绝对误差: 0.8122

1045/1599 ━━━━━━━━━━━━━ [37m━━━━━━━ 2:21 256毫秒/步 - 损失: 1.0308 - 平均绝对误差: 0.8122

1046/1599 ━━━━━━━━━━━━━ [37m━━━━━━━ 2:21 256毫秒/步 - 损失: 1.0308 - 平均绝对误差: 0.8122

1047/1599 ━━━━━━━━━━━━━ [37m━━━━━━━ 2:21 256毫秒/步 - 损失: 1.0307 - 平均绝对误差: 0.8122

1048/1599 ━━━━━━━━━━━━━ [37m━━━━━━━ 2:21 256毫秒/步 - 损失: 1.0307 - 平均绝对误差: 0.8122

1049/1599 ━━━━━━━━━━━━━ [37m━━━━━━━ 2:20 256毫秒/步 - 损失: 1.0307 - 平均绝对误差: 0.8122

1050/1599 ━━━━━━━━━━━━━ [37m━━━━━━━ 2:20 256毫秒/步 - 损失: 1.0307 - 平均绝对误差: 0.8122

1051/1599 ━━━━━━━━━━━━━ [37m━━━━━━━ 2:20 256毫秒/步 - 损失: 1.0307 - 平均绝对误差: 0.8122

1052/1599 ━━━━━━━━━━━━━ [37m━━━━━━━ 2:20 256毫秒/步 - 损失: 1.0306 - 平均绝对误差: 0.8122

1053/1599 ━━━━━━━━━━━━━ [37m━━━━━━━ 2:20 257毫秒/步 - 损失: 1.0306 - 平均绝对误差: 0.8122

1054/1599 ━━━━━━━━━━━━━ [37m━━━━━━━ 2:19 257毫秒/步 - 损失: 1.0306 - 平均绝对误差: 0.8122

1055/1599 ━━━━━━━━━━━━━ [37m━━━━━━━ 2:19 257毫秒/步 - 损失: 1.0306 - 平均绝对误差: 0.8122

1056/1599 ━━━━━━━━━━━━━ [37m━━━━━━━ 2:19 257毫秒/步 - 损失: 1.0306 - 平均绝对误差: 0.8121

1057/1599 ━━━━━━━━━━━━━ [37m━━━━━━━ 2:19 257毫秒/步 - 损失: 1.0306 - 平均绝对误差: 0.8121

1058/1599 ━━━━━━━━━━━━━ [37m━━━━━━━ 2:19 257毫秒/步 - 损失: 1.0305 - 平均绝对误差: 0.8121

1059/1599 ━━━━━━━━━━━━━ [37m━━━━━━━ 2:18 257毫秒/步 - 损失: 1.0305 - 平均绝对误差: 0.8121

1060/1599 ━━━━━━━━━━━━━ [37m━━━━━━━ 2:18 257毫秒/步 - 损失: 1.0305 - 平均绝对误差: 0.8121

1061/1599 ━━━━━━━━━━━━━ [37m━━━━━━━ 2:18 257毫秒/步 - 损失: 1.0305 - 平均绝对误差: 0.8121

1062/1599 ━━━━━━━━━━━━━ [37m━━━━━━━ 2:18 257毫秒/步 - 损失: 1.0305 - 平均绝对误差: 0.8121

1063/1599 ━━━━━━━━━━━━━ [37m━━━━━━━ 2:17 257毫秒/步 - 损失: 1.0304 - 平均绝对误差: 0.8121

1064/1599 ━━━━━━━━━━━━━ [37m━━━━━━━ 2:17 257毫秒/步 - 损失: 1.0304 - 平均绝对误差: 0.8121

1065/1599 ━━━━━━━━━━━━━ [37m━━━━━━━ 2:17 257毫秒/步 - 损失: 1.0304 - 平均绝对误差: 0.8121

1066/1599 ━━━━━━━━━━━━━ [37m━━━━━━━ 2:17 258毫秒/步 - 损失: 1.0304 - 平均绝对误差: 0.8121

1067/1599 ━━━━━━━━━━━━━ [37m━━━━━━━ 2:17 258毫秒/步 - 损失: 1.0304 - 平均绝对误差: 0.8121

1068/1599 ━━━━━━━━━━━━━ [37m━━━━━━━ 2:16 258毫秒/步 - 损失: 1.0304 - 平均绝对误差: 0.8120

1069/1599 ━━━━━━━━━━━━━ [37m━━━━━━━ 2:16 258毫秒/步 - 损失: 1.0303 - 平均绝对误差: 0.8120

1070/1599 ━━━━━━━━━━━━━ [37m━━━━━━━ 2:16 258毫秒/步 - 损失: 1.0303 - 平均绝对误差: 0.8120

1071/1599 ━━━━━━━━━━━━━ [37m━━━━━━━ 2:16 258毫秒/步 - 损失: 1.0303 - 平均绝对误差: 0.8120

1072/1599 ━━━━━━━━━━━━━ [37m━━━━━━━ 2:15 258毫秒/步 - 损失: 1.0303 - 平均绝对误差: 0.8120

1073/1599 ━━━━━━━━━━━━━ [37m━━━━━━━ 2:15 258毫秒/步 - 损失: 1.0303 - 平均绝对误差: 0.8120

1074/1599 ━━━━━━━━━━━━━ [37m━━━━━━━ 2:15 258毫秒/步 - 损失: 1.0302 - 平均绝对误差: 0.8120

1075/1599 ━━━━━━━━━━━━━ [37m━━━━━━━ 2:15 258毫秒/步 - 损失: 1.0302 - 平均绝对误差: 0.8120

1076/1599 ━━━━━━━━━━━━━ [37m━━━━━━━ 2:14 258毫秒/步 - 损失: 1.0302 - 平均绝对误差: 0.8120

1077/1599 ━━━━━━━━━━━━━ [37m━━━━━━━ 2:14 258毫秒/步 - 损失: 1.0302 - 平均绝对误差: 0.8120

1078/1599 ━━━━━━━━━━━━━ [37m━━━━━━━ 2:14 258毫秒/步 - 损失: 1.0302 - 平均绝对误差: 0.8120

1079/1599 ━━━━━━━━━━━━━ [37m━━━━━━━ 2:14 258毫秒/步 - 损失: 1.0302 - 平均绝对误差: 0.8120

1080/1599 ━━━━━━━━━━━━━ [37m━━━━━━━ 2:13 258毫秒/步 - 损失: 1.0301 - 平均绝对误差: 0.8120

1081/1599 ━━━━━━━━━━━━━ [37m━━━━━━━ 2:13 258毫秒/步 - 损失: 1.0301 - 平均绝对误差: 0.8119

1082/1599 ━━━━━━━━━━━━━ [37m━━━━━━━ 2:13 258毫秒/步 - 损失: 1.0301 - 平均绝对误差: 0.8119

1083/1599 ━━━━━━━━━━━━━ [37m━━━━━━━ 2:13 258毫秒/步 - 损失: 1.0301 - 平均绝对误差: 0.8119

1084/1599 ━━━━━━━━━━━━━ [37m━━━━━━━ 2:13 258毫秒/步 - 损失: 1.0301 - 平均绝对误差: 0.8119

1085/1599 ━━━━━━━━━━━━━ [37m━━━━━━━ 2:12 258毫秒/步 - 损失: 1.0301 - 平均绝对误差: 0.8119

1086/1599 ━━━━━━━━━━━━━ [37m━━━━━━━ 2:12 258毫秒/步 - 损失: 1.0300 - 平均绝对误差: 0.8119

1087/1599 ━━━━━━━━━━━━━ [37m━━━━━━━ 2:12 258毫秒/步 - 损失: 1.0300 - 平均绝对误差: 0.8119

1088/1599 ━━━━━━━━━━━━━ [37m━━━━━━━ 2:12 258毫秒/步 - 损失: 1.0300 - 平均绝对误差: 0.8119

1089/1599 ━━━━━━━━━━━━━ [37m━━━━━━━ 2:11 258毫秒/步 - 损失: 1.0300 - 平均绝对误差: 0.8119

1090/1599 ━━━━━━━━━━━━━ [37m━━━━━━━ 2:11 259毫秒/步 - 损失: 1.0300 - 平均绝对误差: 0.8119

1091/1599 ━━━━━━━━━━━━━ [37m━━━━━━━ 2:11 259毫秒/步 - 损失: 1.0299 - 平均绝对误差: 0.8119

1092/1599 ━━━━━━━━━━━━━ [37m━━━━━━━ 2:11 259毫秒/步 - 损失: 1.0299 - 平均绝对误差: 0.8119

1093/1599 ━━━━━━━━━━━━━ [37m━━━━━━━ 2:10 259毫秒/步 - 损失: 1.0299 - 平均绝对误差: 0.8118

1094/1599 ━━━━━━━━━━━━━ [37m━━━━━━━ 2:10 259毫秒/步 - 损失: 1.0299 - 平均绝对误差: 0.8118

1095/1599 ━━━━━━━━━━━━━ [37m━━━━━━━ 2:10 259毫秒/步 - 损失: 1.0299 - 平均绝对误差: 0.8118

1096/1599 ━━━━━━━━━━━━━ [37m━━━━━━━ 2:10 259毫秒/步 - 损失: 1.0299 - 平均绝对误差: 0.8118

1097/1599 ━━━━━━━━━━━━━ [37m━━━━━━━ 2:09 259毫秒/步 - 损失: 1.0298 - 平均绝对误差: 0.8118

1098/1599 ━━━━━━━━━━━━━ [37m━━━━━━━ 2:09 259毫秒/步 - 损失: 1.0298 - 平均绝对误差: 0.8118

1099/1599 ━━━━━━━━━━━━━ [37m━━━━━━━ 2:09 259毫秒/步 - 损失: 1.0298 - 平均绝对误差: 0.8118

1100/1599 ━━━━━━━━━━━━━ [37m━━━━━━━ 2:09 259毫秒/步 - 损失: 1.0298 - 平均绝对误差: 0.8118

1101/1599 ━━━━━━━━━━━━━ [37m━━━━━━━ 2:08 259毫秒/步 - 损失: 1.0298 - 平均绝对误差: 0.8118

1102/1599 ━━━━━━━━━━━━━ [37m━━━━━━━ 2:08 259毫秒/步 - 损失: 1.0298 - 平均绝对误差: 0.8118

1103/1599 ━━━━━━━━━━━━━ [37m━━━━━━━ 2:08 259毫秒/步 - 损失: 1.0297 - 平均绝对误差: 0.8118

1104/1599 ━━━━━━━━━━━━━ [37m━━━━━━━ 2:08 259毫秒/步 - 损失: 1.0297 - 平均绝对误差: 0.8118

1105/1599 ━━━━━━━━━━━━━ [37m━━━━━━━ 2:07 259毫秒/步 - 损失: 1.0297 - 平均绝对误差: 0.8117

1106/1599 ━━━━━━━━━━━━━ [37m━━━━━━━ 2:07 259毫秒/步 - 损失: 1.0297 - 平均绝对误差: 0.8117

1107/1599 ━━━━━━━━━━━━━ [37m━━━━━━━ 2:07 259毫秒/步 - 损失: 1.0297 - 平均绝对误差: 0.8117

1108/1599 ━━━━━━━━━━━━━ [37m━━━━━━━ 2:07 259毫秒/步 - 损失: 1.0297 - 平均绝对误差: 0.8117

1109/1599 ━━━━━━━━━━━━━ [37m━━━━━━━ 2:06 259毫秒/步 - 损失: 1.0296 - 平均绝对误差: 0.8117

1110/1599 ━━━━━━━━━━━━━ [37m━━━━━━━ 2:06 259毫秒/步 - 损失: 1.0296 - 平均绝对误差: 0.8117

1111/1599 ━━━━━━━━━━━━━ [37m━━━━━━━ 2:06 259毫秒/步 - 损失: 1.0296 - 平均绝对误差: 0.8117

1112/1599 ━━━━━━━━━━━━━ [37m━━━━━━━ 2:06 259毫秒/步 - 损失: 1.0296 - 平均绝对误差: 0.8117

1113/1599 ━━━━━━━━━━━━━ [37m━━━━━━━ 2:05 259毫秒/步 - 损失: 1.0296 - 平均绝对误差: 0.8117

1114/1599 ━━━━━━━━━━━━━ [37m━━━━━━━ 2:05 259毫秒/步 - 损失: 1.0295 - 平均绝对误差: 0.8117

1115/1599 ━━━━━━━━━━━━━ [37m━━━━━━━ 2:05 259毫秒/步 - 损失: 1.0295 - 平均绝对误差: 0.8117

1116/1599 ━━━━━━━━━━━━━ [37m━━━━━━━ 2:05 259毫秒/步 - 损失: 1.0295 - 平均绝对误差: 0.8117

1117/1599 ━━━━━━━━━━━━━ [37m━━━━━━━ 2:04 259毫秒/步 - 损失: 1.0295 - 平均绝对误差: 0.8117

1118/1599 ━━━━━━━━━━━━━ [37m━━━━━━━ 2:04 259毫秒/步 - 损失: 1.0295 - 平均绝对误差: 0.8116

1119/1599 ━━━━━━━━━━━━━ [37m━━━━━━━ 2:04 259毫秒/步 - 损失: 1.0295 - 平均绝对误差: 0.8116

1120/1599 ━━━━━━━━━━━━━━ [37m━━━━━━ 2:04 259毫秒/步 - 损失: 1.0294 - 平均绝对误差: 0.8116

1121/1599 ━━━━━━━━━━━━━━ [37m━━━━━━ 2:04 259毫秒/步 - 损失: 1.0294 - 平均绝对误差: 0.8116

1122/1599 ━━━━━━━━━━━━━━ [37m━━━━━━ 2:03 259毫秒/步 - 损失: 1.0294 - 平均绝对误差: 0.8116

1123/1599 ━━━━━━━━━━━━━━ [37m━━━━━━ 2:03 260毫秒/步 - 损失: 1.0294 - 平均绝对误差: 0.8116

1124/1599 ━━━━━━━━━━━━━━ [37m━━━━━━ 2:03 260毫秒/步 - 损失: 1.0294 - 平均绝对误差: 0.8116

1125/1599 ━━━━━━━━━━━━━━ [37m━━━━━━ 2:03 260毫秒/步 - 损失: 1.0293 - 平均绝对误差: 0.8116

1126/1599 ━━━━━━━━━━━━━━ [37m━━━━━━ 2:02 260毫秒/步 - 损失: 1.0293 - 平均绝对误差: 0.8116

1127/1599 ━━━━━━━━━━━━━━ [37m━━━━━━ 2:02 260毫秒/步 - 损失: 1.0293 - 平均绝对误差: 0.8116

1128/1599 ━━━━━━━━━━━━━━ [37m━━━━━━ 2:02 260毫秒/步 - 损失: 1.0293 - 平均绝对误差: 0.8116

1129/1599 ━━━━━━━━━━━━━━ [37m━━━━━━ 2:02 260毫秒/步 - 损失: 1.0293 - 平均绝对误差: 0.8115

1130/1599 ━━━━━━━━━━━━━━ [37m━━━━━━ 2:01 260毫秒/步 - 损失: 1.0292 - 平均绝对误差: 0.8115

1131/1599 ━━━━━━━━━━━━━━ [37m━━━━━━ 2:01 260毫秒/步 - 损失: 1.0292 - 平均绝对误差: 0.8115

1132/1599 ━━━━━━━━━━━━━━ [37m━━━━━━ 2:01 260毫秒/步 - 损失: 1.0292 - 平均绝对误差: 0.8115

1133/1599 ━━━━━━━━━━━━━━ [37m━━━━━━ 2:00 260毫秒/步 - 损失: 1.0292 - 平均绝对误差: 0.8115

1134/1599 ━━━━━━━━━━━━━━ [37m━━━━━━ 2:00 260毫秒/步 - 损失: 1.0292 - 平均绝对误差: 0.8115

1135/1599 ━━━━━━━━━━━━━━ [37m━━━━━━ 2:00 260毫秒/步 - 损失: 1.0292 - 平均绝对误差: 0.8115

1136/1599 ━━━━━━━━━━━━━━ [37m━━━━━━ 2:00 260毫秒/步 - 损失: 1.0291 - 平均绝对误差: 0.8115

1137/1599 ━━━━━━━━━━━━━━ [37m━━━━━━ 1:59 260毫秒/步 - 损失: 1.0291 - 平均绝对误差: 0.8115

1138/1599 ━━━━━━━━━━━━━━ [37m━━━━━━ 1:59 260毫秒/步 - 损失: 1.0291 - 平均绝对误差: 0.8115

1139/1599 ━━━━━━━━━━━━━━ [37m━━━━━━ 1:59 260毫秒/步 - 损失: 1.0291 - 平均绝对误差: 0.8115

1140/1599 ━━━━━━━━━━━━━━ [37m━━━━━━ 1:59 260毫秒/步 - 损失: 1.0291 - 平均绝对误差: 0.8115

1141/1599 ━━━━━━━━━━━━━━ [37m━━━━━━ 1:58 260毫秒/步 - 损失: 1.0291 - 平均绝对误差: 0.8114

1142/1599 ━━━━━━━━━━━━━━ [37m━━━━━━ 1:58 260毫秒/步 - 损失: 1.0290 - 平均绝对误差: 0.8114

1143/1599 ━━━━━━━━━━━━━━ [37m━━━━━━ 1:58 260毫秒/步 - 损失: 1.0290 - 平均绝对误差: 0.8114

1144/1599 ━━━━━━━━━━━━━━ [37m━━━━━━ 1:58 260毫秒/步 - 损失: 1.0290 - 平均绝对误差: 0.8114

1145/1599 ━━━━━━━━━━━━━━ [37m━━━━━━ 1:57 260毫秒/步 - 损失: 1.0290 - 平均绝对误差: 0.8114

1146/1599 ━━━━━━━━━━━━━━ [37m━━━━━━ 1:57 260毫秒/步 - 损失: 1.0290 - 平均绝对误差: 0.8114

1147/1599 ━━━━━━━━━━━━━━ [37m━━━━━━ 1:57 260毫秒/步 - 损失: 1.0289 - 平均绝对误差: 0.8114

1148/1599 ━━━━━━━━━━━━━━ [37m━━━━━━ 1:57 260毫秒/步 - 损失: 1.0289 - 平均绝对误差: 0.8114

1149/1599 ━━━━━━━━━━━━━━ [37m━━━━━━ 1:56 260毫秒/步 - 损失: 1.0289 - 平均绝对误差: 0.8114

1150/1599 ━━━━━━━━━━━━━━ [37m━━━━━━ 1:56 260毫秒/步 - 损失: 1.0289 - 平均绝对误差: 0.8114

1151/1599 ━━━━━━━━━━━━━━ [37m━━━━━━ 1:56 260毫秒/步 - 损失: 1.0289 - 平均绝对误差: 0.8114

1152/1599 ━━━━━━━━━━━━━━ [37m━━━━━━ 1:56 260毫秒/步 - 损失: 1.0288 - 平均绝对误差: 0.8114

1153/1599 ━━━━━━━━━━━━━━ [37m━━━━━━ 1:55 260毫秒/步 - 损失: 1.0288 - 平均绝对误差: 0.8113

1154/1599 ━━━━━━━━━━━━━━ [37m━━━━━━ 1:55 260毫秒/步 - 损失: 1.0288 - 平均绝对误差: 0.8113

1155/1599 ━━━━━━━━━━━━━━ [37m━━━━━━ 1:55 260毫秒/步 - 损失: 1.0288 - 平均绝对误差: 0.8113

1156/1599 ━━━━━━━━━━━━━━ [37m━━━━━━ 1:55 260毫秒/步 - 损失: 1.0288 - 平均绝对误差: 0.8113

1157/1599 ━━━━━━━━━━━━━━ [37m━━━━━━ 1:54 260毫秒/步 - 损失: 1.0288 - 平均绝对误差: 0.8113

1158/1599 ━━━━━━━━━━━━━━ [37m━━━━━━ 1:54 260毫秒/步 - 损失: 1.0287 - 平均绝对误差: 0.8113

1159/1599 ━━━━━━━━━━━━━━ [37m━━━━━━ 1:54 260毫秒/步 - 损失: 1.0287 - 平均绝对误差: 0.8113

1160/1599 ━━━━━━━━━━━━━━ [37m━━━━━━ 1:54 260毫秒/步 - 损失: 1.0287 - 平均绝对误差: 0.8113

1161/1599 ━━━━━━━━━━━━━━ [37m━━━━━━ 1:53 260毫秒/步 - 损失: 1.0287 - 平均绝对误差: 0.8113

1162/1599 ━━━━━━━━━━━━━━ [37m━━━━━━ 1:53 260毫秒/步 - 损失: 1.0287 - 平均绝对误差: 0.8113

1163/1599 ━━━━━━━━━━━━━━ [37m━━━━━━ 1:53 260毫秒/步 - 损失: 1.0286 - 平均绝对误差: 0.8113

1164/1599 ━━━━━━━━━━━━━━ [37m━━━━━━ 1:53 260毫秒/步 - 损失: 1.0286 - 平均绝对误差: 0.8113

1165/1599 ━━━━━━━━━━━━━━ [37m━━━━━━ 1:52 260毫秒/步 - 损失: 1.0286 - 平均绝对误差: 0.8112

1166/1599 ━━━━━━━━━━━━━━ [37m━━━━━━ 1:52 260毫秒/步 - 损失: 1.0286 - 平均绝对误差: 0.8112

1167/1599 ━━━━━━━━━━━━━━ [37m━━━━━━ 1:52 260毫秒/步 - 损失: 1.0286 - 平均绝对误差: 0.8112

1168/1599 ━━━━━━━━━━━━━━ [37m━━━━━━ 1:52 260毫秒/步 - 损失: 1.0286 - 平均绝对误差: 0.8112

1169/1599 ━━━━━━━━━━━━━━ [37m━━━━━━ 1:51 260毫秒/步 - 损失: 1.0285 - 平均绝对误差: 0.8112

1170/1599 ━━━━━━━━━━━━━━ [37m━━━━━━ 1:51 260毫秒/步 - 损失: 1.0285 - 平均绝对误差: 0.8112

1171/1599 ━━━━━━━━━━━━━━ [37m━━━━━━ 1:51 260毫秒/步 - 损失: 1.0285 - 平均绝对误差: 0.8112

1172/1599 ━━━━━━━━━━━━━━ [37m━━━━━━ 1:51 260毫秒/步 - 损失: 1.0285 - 平均绝对误差: 0.8112

1173/1599 ━━━━━━━━━━━━━━ [37m━━━━━━ 1:50 260毫秒/步 - 损失: 1.0285 - 平均绝对误差: 0.8112

1174/1599 ━━━━━━━━━━━━━━ [37m━━━━━━ 1:50 260毫秒/步 - 损失: 1.0285 - 平均绝对误差: 0.8112

1175/1599 ━━━━━━━━━━━━━━ [37m━━━━━━ 1:50 260毫秒/步 - 损失: 1.0284 - 平均绝对误差: 0.8112

1176/1599 ━━━━━━━━━━━━━━ [37m━━━━━━ 1:50 260毫秒/步 - 损失: 1.0284 - 平均绝对误差: 0.8112

1177/1599 ━━━━━━━━━━━━━━ [37m━━━━━━ 1:49 260毫秒/步 - 损失: 1.0284 - 平均绝对误差: 0.8111

1178/1599 ━━━━━━━━━━━━━━ [37m━━━━━━ 1:49 260毫秒/步 - 损失: 1.0284 - 平均绝对误差: 0.8111

1179/1599 ━━━━━━━━━━━━━━ [37m━━━━━━ 1:49 260毫秒/步 - 损失: 1.0284 - 平均绝对误差: 0.8111

1180/1599 ━━━━━━━━━━━━━━ [37m━━━━━━ 1:49 260毫秒/步 - 损失: 1.0283 - 平均绝对误差: 0.8111

1181/1599 ━━━━━━━━━━━━━━ [37m━━━━━━ 1:48 260毫秒/步 - 损失: 1.0283 - 平均绝对误差: 0.8111

1182/1599 ━━━━━━━━━━━━━━ [37m━━━━━━ 1:48 260毫秒/步 - 损失: 1.0283 - 平均绝对误差: 0.8111

1183/1599 ━━━━━━━━━━━━━━ [37m━━━━━━ 1:48 260毫秒/步 - 损失: 1.0283 - 平均绝对误差: 0.8111

1184/1599 ━━━━━━━━━━━━━━ [37m━━━━━━ 1:48 260毫秒/步 - 损失: 1.0283 - 平均绝对误差: 0.8111

1185/1599 ━━━━━━━━━━━━━━ [37m━━━━━━ 1:47 260毫秒/步 - 损失: 1.0283 - 平均绝对误差: 0.8111

1186/1599 ━━━━━━━━━━━━━━ [37m━━━━━━ 1:47 260毫秒/步 - 损失: 1.0282 - 平均绝对误差: 0.8111

1187/1599 ━━━━━━━━━━━━━━ [37m━━━━━━ 1:47 260毫秒/步 - 损失: 1.0282 - 平均绝对误差: 0.8111

1188/1599 ━━━━━━━━━━━━━━ [37m━━━━━━ 1:46 260毫秒/步 - 损失: 1.0282 - 平均绝对误差: 0.8111

1189/1599 ━━━━━━━━━━━━━━ [37m━━━━━━ 1:46 260毫秒/步 - 损失: 1.0282 - 平均绝对误差: 0.8110

1190/1599 ━━━━━━━━━━━━━━ [37m━━━━━━ 1:46 260毫秒/步 - 损失: 1.0282 - 平均绝对误差: 0.8110

1191/1599 ━━━━━━━━━━━━━━ [37m━━━━━━ 1:46 260毫秒/步 - 损失: 1.0281 - 平均绝对误差: 0.8110

1192/1599 ━━━━━━━━━━━━━━ [37m━━━━━━ 1:45 260毫秒/步 - 损失: 1.0281 - 平均绝对误差: 0.8110

1193/1599 ━━━━━━━━━━━━━━ [37m━━━━━━ 1:45 260毫秒/步 - 损失: 1.0281 - 平均绝对误差: 0.8110

1194/1599 ━━━━━━━━━━━━━━ [37m━━━━━━ 1:45 260毫秒/步 - 损失: 1.0281 - 平均绝对误差: 0.8110

1195/1599 ━━━━━━━━━━━━━━ [37m━━━━━━ 1:45 260毫秒/步 - 损失: 1.0281 - 平均绝对误差: 0.8110

1196/1599 ━━━━━━━━━━━━━━ [37m━━━━━━ 1:44 260毫秒/步 - 损失: 1.0281 - 平均绝对误差: 0.8110

1197/1599 ━━━━━━━━━━━━━━ [37m━━━━━━ 1:44 260毫秒/步 - 损失: 1.0280 - 平均绝对误差: 0.8110

1198/1599 ━━━━━━━━━━━━━━ [37m━━━━━━ 1:44 260毫秒/步 - 损失: 1.0280 - 平均绝对误差: 0.8110

1199/1599 ━━━━━━━━━━━━━━ [37m━━━━━━ 1:44 260毫秒/步 - 损失: 1.0280 - 平均绝对误差: 0.8110

1200/1599 ━━━━━━━━━━━━━━━ [37m━━━━━ 1:43 260毫秒/步 - 损失: 1.0280 - 平均绝对误差: 0.8110

1201/1599 ━━━━━━━━━━━━━━━ [37m━━━━━ 1:43 260毫秒/步 - 损失: 1.0280 - 平均绝对误差: 0.8110

1202/1599 ━━━━━━━━━━━━━━━ [37m━━━━━ 1:43 261毫秒/步 - 损失: 1.0280 - 平均绝对误差: 0.8109

1203/1599 ━━━━━━━━━━━━━━━ [37m━━━━━ 1:43 261毫秒/步 - 损失: 1.0279 - 平均绝对误差: 0.8109

1204/1599 ━━━━━━━━━━━━━━━ [37m━━━━━ 1:42 261毫秒/步 - 损失: 1.0279 - 平均绝对误差: 0.8109

1205/1599 ━━━━━━━━━━━━━━━ [37m━━━━━ 1:42 261毫秒/步 - 损失: 1.0279 - 平均绝对误差: 0.8109

1206/1599 ━━━━━━━━━━━━━━━ [37m━━━━━ 1:42 261毫秒/步 - 损失: 1.0279 - 平均绝对误差: 0.8109

1207/1599 ━━━━━━━━━━━━━━━ [37m━━━━━ 1:42 261毫秒/步 - 损失: 1.0279 - 平均绝对误差: 0.8109

1208/1599 ━━━━━━━━━━━━━━━ [37m━━━━━ 1:41 261毫秒/步 - 损失: 1.0279 - 平均绝对误差: 0.8109

1209/1599 ━━━━━━━━━━━━━━━ [37m━━━━━ 1:41 261毫秒/步 - 损失: 1.0278 - 平均绝对误差: 0.8109

1210/1599 ━━━━━━━━━━━━━━━ [37m━━━━━ 1:41 261毫秒/步 - 损失: 1.0278 - 平均绝对误差: 0.8109

1211/1599 ━━━━━━━━━━━━━━━ [37m━━━━━ 1:41 261毫秒/步 - 损失: 1.0278 - 平均绝对误差: 0.8109

1212/1599 ━━━━━━━━━━━━━━━ [37m━━━━━ 1:40 261毫秒/步 - 损失: 1.0278 - 平均绝对误差: 0.8109

1213/1599 ━━━━━━━━━━━━━━━ [37m━━━━━ 1:40 261毫秒/步 - 损失: 1.0278 - 平均绝对误差: 0.8109

1214/1599 ━━━━━━━━━━━━━━━ [37m━━━━━ 1:40 261毫秒/步 - 损失: 1.0278 - 平均绝对误差: 0.8108

1215/1599 ━━━━━━━━━━━━━━━ [37m━━━━━ 1:40 261毫秒/步 - 损失: 1.0277 - 平均绝对误差: 0.8108

1216/1599 ━━━━━━━━━━━━━━━ [37m━━━━━ 1:39 261毫秒/步 - 损失: 1.0277 - 平均绝对误差: 0.8108

1217/1599 ━━━━━━━━━━━━━━━ [37m━━━━━ 1:39 261毫秒/步 - 损失: 1.0277 - 平均绝对误差: 0.8108

1218/1599 ━━━━━━━━━━━━━━━ [37m━━━━━ 1:39 261毫秒/步 - 损失: 1.0277 - 平均绝对误差: 0.8108

1219/1599 ━━━━━━━━━━━━━━━ [37m━━━━━ 1:39 261毫秒/步 - 损失: 1.0277 - 平均绝对误差: 0.8108

1220/1599 ━━━━━━━━━━━━━━━ [37m━━━━━ 1:38 261毫秒/步 - 损失: 1.0276 - 平均绝对误差: 0.8108

1221/1599 ━━━━━━━━━━━━━━━ [37m━━━━━ 1:38 261毫秒/步 - 损失: 1.0276 - 平均绝对误差: 0.8108

1222/1599 ━━━━━━━━━━━━━━━ [37m━━━━━ 1:38 261毫秒/步 - 损失: 1.0276 - 平均绝对误差: 0.8108

1223/1599 ━━━━━━━━━━━━━━━ [37m━━━━━ 1:38 261毫秒/步 - 损失: 1.0276 - 平均绝对误差: 0.8108

1224/1599 ━━━━━━━━━━━━━━━ [37m━━━━━ 1:37 261毫秒/步 - 损失: 1.0276 - 平均绝对误差: 0.8108

1225/1599 ━━━━━━━━━━━━━━━ [37m━━━━━ 1:37 261毫秒/步 - 损失: 1.0276 - 平均绝对误差: 0.8108

1226/1599 ━━━━━━━━━━━━━━━ [37m━━━━━ 1:37 261毫秒/步 - 损失: 1.0275 - 平均绝对误差: 0.8108

1227/1599 ━━━━━━━━━━━━━━━ [37m━━━━━ 1:36 261毫秒/步 - 损失: 1.0275 - 平均绝对误差: 0.8107

1228/1599 ━━━━━━━━━━━━━━━ [37m━━━━━ 1:36 261毫秒/步 - 损失: 1.0275 - 平均绝对误差: 0.8107

1229/1599 ━━━━━━━━━━━━━━━ [37m━━━━━ 1:36 261毫秒/步 - 损失: 1.0275 - 平均绝对误差: 0.8107

1230/1599 ━━━━━━━━━━━━━━━ [37m━━━━━ 1:36 261毫秒/步 - 损失: 1.0275 - 平均绝对误差: 0.8107

1231/1599 ━━━━━━━━━━━━━━━ [37m━━━━━ 1:35 261毫秒/步 - 损失: 1.0275 - 平均绝对误差: 0.8107

1232/1599 ━━━━━━━━━━━━━━━ [37m━━━━━ 1:35 261毫秒/步 - 损失: 1.0274 - 平均绝对误差: 0.8107

1233/1599 ━━━━━━━━━━━━━━━ [37m━━━━━ 1:35 261毫秒/步 - 损失: 1.0274 - 平均绝对误差: 0.8107

1234/1599 ━━━━━━━━━━━━━━━ [37m━━━━━ 1:35 261毫秒/步 - 损失: 1.0274 - 平均绝对误差: 0.8107

1235/1599 ━━━━━━━━━━━━━━━ [37m━━━━━ 1:34 261毫秒/步 - 损失: 1.0274 - 平均绝对误差: 0.8107

1236/1599 ━━━━━━━━━━━━━━━ [37m━━━━━ 1:34 261毫秒/步 - 损失: 1.0274 - 平均绝对误差: 0.8107

1237/1599 ━━━━━━━━━━━━━━━ [37m━━━━━ 1:34 261毫秒/步 - 损失: 1.0274 - 平均绝对误差: 0.8107

1238/1599 ━━━━━━━━━━━━━━━ [37m━━━━━ 1:34 261毫秒/步 - 损失: 1.0273 - 平均绝对误差: 0.8107

1239/1599 ━━━━━━━━━━━━━━━ [37m━━━━━ 1:33 261毫秒/步 - 损失: 1.0273 - 平均绝对误差: 0.8107

1240/1599 ━━━━━━━━━━━━━━━ [37m━━━━━ 1:33 261毫秒/步 - 损失: 1.0273 - 平均绝对误差: 0.8106

1241/1599 ━━━━━━━━━━━━━━━ [37m━━━━━ 1:33 261毫秒/步 - 损失: 1.0273 - 平均绝对误差: 0.8106

1242/1599 ━━━━━━━━━━━━━━━ [37m━━━━━ 1:33 261毫秒/步 - 损失: 1.0273 - 平均绝对误差: 0.8106

1243/1599 ━━━━━━━━━━━━━━━ [37m━━━━━ 1:33 261毫秒/步 - 损失: 1.0273 - 平均绝对误差: 0.8106

1244/1599 ━━━━━━━━━━━━━━━ [37m━━━━━ 1:32 261毫秒/步 - 损失: 1.0272 - 平均绝对误差: 0.8106

1245/1599 ━━━━━━━━━━━━━━━ [37m━━━━━ 1:32 261毫秒/步 - 损失: 1.0272 - 平均绝对误差: 0.8106

1246/1599 ━━━━━━━━━━━━━━━ [37m━━━━━ 1:32 261毫秒/步 - 损失: 1.0272 - 平均绝对误差: 0.8106

1247/1599 ━━━━━━━━━━━━━━━ [37m━━━━━ 1:31 261毫秒/步 - 损失: 1.0272 - 平均绝对误差: 0.8106

1248/1599 ━━━━━━━━━━━━━━━ [37m━━━━━ 1:31 261毫秒/步 - 损失: 1.0272 - 平均绝对误差: 0.8106

1249/1599 ━━━━━━━━━━━━━━━ [37m━━━━━ 1:31 261毫秒/步 - 损失: 1.0272 - 平均绝对误差: 0.8106

1250/1599 ━━━━━━━━━━━━━━━ [37m━━━━━ 1:31 261毫秒/步 - 损失: 1.0271 - 平均绝对误差: 0.8106

1251/1599 ━━━━━━━━━━━━━━━ [37m━━━━━ 1:30 261毫秒/步 - 损失: 1.0271 - 平均绝对误差: 0.8106

1252/1599 ━━━━━━━━━━━━━━━ [37m━━━━━ 1:30 261毫秒/步 - 损失: 1.0271 - 平均绝对误差: 0.8106

1253/1599 ━━━━━━━━━━━━━━━ [37m━━━━━ 1:30 261毫秒/步 - 损失: 1.0271 - 平均绝对误差: 0.8105

1254/1599 ━━━━━━━━━━━━━━━ [37m━━━━━ 1:30 261毫秒/步 - 损失: 1.0271 - 平均绝对误差: 0.8105

1255/1599 ━━━━━━━━━━━━━━━ [37m━━━━━ 1:29 261毫秒/步 - 损失: 1.0271 - 平均绝对误差: 0.8105

1256/1599 ━━━━━━━━━━━━━━━ [37m━━━━━ 1:29 261毫秒/步 - 损失: 1.0270 - 平均绝对误差: 0.8105

1257/1599 ━━━━━━━━━━━━━━━ [37m━━━━━ 1:29 261毫秒/步 - 损失: 1.0270 - 平均绝对误差: 0.8105

1258/1599 ━━━━━━━━━━━━━━━ [37m━━━━━ 1:29 261毫秒/步 - 损失: 1.0270 - 平均绝对误差: 0.8105

1259/1599 ━━━━━━━━━━━━━━━ [37m━━━━━ 1:28 261毫秒/步 - 损失: 1.0270 - 平均绝对误差: 0.8105

1260/1599 ━━━━━━━━━━━━━━━ [37m━━━━━ 1:28 261毫秒/步 - 损失: 1.0270 - 平均绝对误差: 0.8105

1261/1599 ━━━━━━━━━━━━━━━ [37m━━━━━ 1:28 261毫秒/步 - 损失: 1.0270 - 平均绝对误差: 0.8105

1262/1599 ━━━━━━━━━━━━━━━ [37m━━━━━ 1:28 261毫秒/步 - 损失: 1.0269 - 平均绝对误差: 0.8105

1263/1599 ━━━━━━━━━━━━━━━ [37m━━━━━ 1:27 261毫秒/步 - 损失: 1.0269 - 平均绝对误差: 0.8105

1264/1599 ━━━━━━━━━━━━━━━ [37m━━━━━ 1:27 261毫秒/步 - 损失: 1.0269 - 平均绝对误差: 0.8105

1265/1599 ━━━━━━━━━━━━━━━ [37m━━━━━ 1:27 261毫秒/步 - 损失: 1.0269 - 平均绝对误差: 0.8105

1266/1599 ━━━━━━━━━━━━━━━ [37m━━━━━ 1:27 261毫秒/步 - 损失: 1.0269 - 平均绝对误差: 0.8104

1267/1599 ━━━━━━━━━━━━━━━ [37m━━━━━ 1:26 261毫秒/步 - 损失: 1.0269 - 平均绝对误差: 0.8104

1268/1599 ━━━━━━━━━━━━━━━ [37m━━━━━ 1:26 261毫秒/步 - 损失: 1.0268 - 平均绝对误差: 0.8104

1269/1599 ━━━━━━━━━━━━━━━ [37m━━━━━ 1:26 261毫秒/步 - 损失: 1.0268 - 平均绝对误差: 0.8104

1270/1599 ━━━━━━━━━━━━━━━ [37m━━━━━ 1:26 261毫秒/步 - 损失: 1.0268 - 平均绝对误差: 0.8104

1271/1599 ━━━━━━━━━━━━━━━ [37m━━━━━ 1:25 262毫秒/步 - 损失: 1.0268 - 平均绝对误差: 0.8104

1272/1599 ━━━━━━━━━━━━━━━ [37m━━━━━ 1:25 262毫秒/步 - 损失: 1.0268 - 平均绝对误差: 0.8104

1273/1599 ━━━━━━━━━━━━━━━ [37m━━━━━ 1:25 262毫秒/步 - 损失: 1.0268 - 平均绝对误差: 0.8104

1274/1599 ━━━━━━━━━━━━━━━ [37m━━━━━ 1:25 262毫秒/步 - 损失: 1.0268 - 平均绝对误差: 0.8104

1275/1599 ━━━━━━━━━━━━━━━ [37m━━━━━ 1:24 262毫秒/步 - 损失: 1.0267 - 平均绝对误差: 0.8104

1276/1599 ━━━━━━━━━━━━━━━ [37m━━━━━ 1:24 262毫秒/步 - 损失: 1.0267 - 平均绝对误差: 0.8104

1277/1599 ━━━━━━━━━━━━━━━ [37m━━━━━ 1:24 262毫秒/步 - 损失: 1.0267 - 平均绝对误差: 0.8104

1278/1599 ━━━━━━━━━━━━━━━ [37m━━━━━ 1:24 262毫秒/步 - 损失: 1.0267 - 平均绝对误差: 0.8104

1279/1599 ━━━━━━━━━━━━━━━ [37m━━━━━ 1:23 262毫秒/步 - 损失: 1.0267 - 平均绝对误差: 0.8103

1280/1599 ━━━━━━━━━━━━━━━━ [37m━━━━ 1:23 262毫秒/步 - 损失: 1.0267 - 平均绝对误差: 0.8103

1281/1599 ━━━━━━━━━━━━━━━━ [37m━━━━ 1:23 262毫秒/步 - 损失: 1.0266 - 平均绝对误差: 0.8103

1282/1599 ━━━━━━━━━━━━━━━━ [37m━━━━ 1:23 262毫秒/步 - 损失: 1.0266 - 平均绝对误差: 0.8103

1283/1599 ━━━━━━━━━━━━━━━━ [37m━━━━ 1:22 262毫秒/步 - 损失: 1.0266 - 平均绝对误差: 0.8103

1284/1599 ━━━━━━━━━━━━━━━━ [37m━━━━ 1:22 262毫秒/步 - 损失: 1.0266 - 平均绝对误差: 0.8103

1285/1599 ━━━━━━━━━━━━━━━━ [37m━━━━ 1:22 262毫秒/步 - 损失: 1.0266 - 平均绝对误差: 0.8103

1286/1599 ━━━━━━━━━━━━━━━━ [37m━━━━ 1:22 262毫秒/步 - 损失: 1.0266 - 平均绝对误差: 0.8103

1287/1599 ━━━━━━━━━━━━━━━━ [37m━━━━ 1:21 262毫秒/步 - 损失: 1.0265 - 平均绝对误差: 0.8103

1288/1599 ━━━━━━━━━━━━━━━━ [37m━━━━ 1:21 262毫秒/步 - 损失: 1.0265 - 平均绝对误差: 0.8103

1289/1599 ━━━━━━━━━━━━━━━━ [37m━━━━ 1:21 262毫秒/步 - 损失: 1.0265 - 平均绝对误差: 0.8103

1290/1599 ━━━━━━━━━━━━━━━━ [37m━━━━ 1:20 262毫秒/步 - 损失: 1.0265 - 平均绝对误差: 0.8103

1291/1599 ━━━━━━━━━━━━━━━━ [37m━━━━ 1:20 262毫秒/步 - 损失: 1.0265 - 平均绝对误差: 0.8103

1292/1599 ━━━━━━━━━━━━━━━━ [37m━━━━ 1:20 262毫秒/步 - 损失: 1.0265 - 平均绝对误差: 0.8103

1293/1599 ━━━━━━━━━━━━━━━━ [37m━━━━ 1:20 262毫秒/步 - 损失: 1.0265 - 平均绝对误差: 0.8102

1294/1599 ━━━━━━━━━━━━━━━━ [37m━━━━ 1:19 262毫秒/步 - 损失: 1.0264 - 平均绝对误差: 0.8102

1295/1599 ━━━━━━━━━━━━━━━━ [37m━━━━ 1:19 262毫秒/步 - 损失: 1.0264 - 平均绝对误差: 0.8102

1296/1599 ━━━━━━━━━━━━━━━━ [37m━━━━ 1:19 262毫秒/步 - 损失: 1.0264 - 平均绝对误差: 0.8102

1297/1599 ━━━━━━━━━━━━━━━━ [37m━━━━ 1:19 262毫秒/步 - 损失: 1.0264 - 平均绝对误差: 0.8102

1298/1599 ━━━━━━━━━━━━━━━━ [37m━━━━ 1:18 262毫秒/步 - 损失: 1.0264 - 平均绝对误差: 0.8102

1299/1599 ━━━━━━━━━━━━━━━━ [37m━━━━ 1:18 262毫秒/步 - 损失: 1.0264 - 平均绝对误差: 0.8102

1300/1599 ━━━━━━━━━━━━━━━━ [37m━━━━ 1:18 262毫秒/步 - 损失: 1.0263 - 平均绝对误差: 0.8102

1301/1599 ━━━━━━━━━━━━━━━━ [37m━━━━ 1:18 262毫秒/步 - 损失: 1.0263 - 平均绝对误差: 0.8102

1302/1599 ━━━━━━━━━━━━━━━━ [37m━━━━ 1:17 262毫秒/步 - 损失: 1.0263 - 平均绝对误差: 0.8102

1303/1599 ━━━━━━━━━━━━━━━━ [37m━━━━ 1:17 262毫秒/步 - 损失: 1.0263 - 平均绝对误差: 0.8102

1304/1599 ━━━━━━━━━━━━━━━━ [37m━━━━ 1:17 262毫秒/步 - 损失: 1.0263 - 平均绝对误差: 0.8102

1305/1599 ━━━━━━━━━━━━━━━━ [37m━━━━ 1:17 262毫秒/步 - 损失: 1.0263 - 平均绝对误差: 0.8102

1306/1599 ━━━━━━━━━━━━━━━━ [37m━━━━ 1:16 262毫秒/步 - 损失: 1.0263 - 平均绝对误差: 0.8102

1307/1599 ━━━━━━━━━━━━━━━━ [37m━━━━ 1:16 262毫秒/步 - 损失: 1.0262 - 平均绝对误差: 0.8101

1308/1599 ━━━━━━━━━━━━━━━━ [37m━━━━ 1:16 262毫秒/步 - 损失: 1.0262 - 平均绝对误差: 0.8101

1309/1599 ━━━━━━━━━━━━━━━━ [37m━━━━ 1:16 262毫秒/步 - 损失: 1.0262 - 平均绝对误差: 0.8101

1310/1599 ━━━━━━━━━━━━━━━━ [37m━━━━ 1:15 262毫秒/步 - 损失: 1.0262 - 平均绝对误差: 0.8101

1311/1599 ━━━━━━━━━━━━━━━━ [37m━━━━ 1:15 262毫秒/步 - 损失: 1.0262 - 平均绝对误差: 0.8101

1312/1599 ━━━━━━━━━━━━━━━━ [37m━━━━ 1:15 262毫秒/步 - 损失: 1.0262 - 平均绝对误差: 0.8101

1313/1599 ━━━━━━━━━━━━━━━━ [37m━━━━ 1:14 262毫秒/步 - 损失: 1.0261 - 平均绝对误差: 0.8101

1314/1599 ━━━━━━━━━━━━━━━━ [37m━━━━ 1:14 262毫秒/步 - 损失: 1.0261 - 平均绝对误差: 0.8101

1315/1599 ━━━━━━━━━━━━━━━━ [37m━━━━ 1:14 262毫秒/步 - 损失: 1.0261 - 平均绝对误差: 0.8101

1316/1599 ━━━━━━━━━━━━━━━━ [37m━━━━ 1:14 262毫秒/步 - 损失: 1.0261 - 平均绝对误差: 0.8101

1317/1599 ━━━━━━━━━━━━━━━━ [37m━━━━ 1:14 262毫秒/步 - 损失: 1.0261 - 平均绝对误差: 0.8101

1318/1599 ━━━━━━━━━━━━━━━━ [37m━━━━ 1:13 262毫秒/步 - 损失: 1.0261 - 平均绝对误差: 0.8101

1319/1599 ━━━━━━━━━━━━━━━━ [37m━━━━ 1:13 262毫秒/步 - 损失: 1.0261 - 平均绝对误差: 0.8101

1320/1599 ━━━━━━━━━━━━━━━━ [37m━━━━ 1:13 263毫秒/步 - 损失: 1.0260 - 平均绝对误差: 0.8101

1321/1599 ━━━━━━━━━━━━━━━━ [37m━━━━ 1:12 263毫秒/步 - 损失: 1.0260 - 平均绝对误差: 0.8100

1322/1599 ━━━━━━━━━━━━━━━━ [37m━━━━ 1:12 263毫秒/步 - 损失: 1.0260 - 平均绝对误差: 0.8100

1323/1599 ━━━━━━━━━━━━━━━━ [37m━━━━ 1:12 263毫秒/步 - 损失: 1.0260 - 平均绝对误差: 0.8100

1324/1599 ━━━━━━━━━━━━━━━━ [37m━━━━ 1:12 263毫秒/步 - 损失: 1.0260 - 平均绝对误差: 0.8100

1325/1599 ━━━━━━━━━━━━━━━━ [37m━━━━ 1:11 262毫秒/步 - 损失: 1.0260 - 平均绝对误差: 0.8100

1326/1599 ━━━━━━━━━━━━━━━━ [37m━━━━ 1:11 262毫秒/步 - 损失: 1.0259 - 平均绝对误差: 0.8100

1327/1599 ━━━━━━━━━━━━━━━━ [37m━━━━ 1:11 262毫秒/步 - 损失: 1.0259 - 平均绝对误差: 0.8100

1328/1599 ━━━━━━━━━━━━━━━━ [37m━━━━ 1:11 263毫秒/步 - 损失: 1.0259 - 平均绝对误差: 0.8100

1329/1599 ━━━━━━━━━━━━━━━━ [37m━━━━ 1:10 263毫秒/步 - 损失: 1.0259 - 平均绝对误差: 0.8100

1330/1599 ━━━━━━━━━━━━━━━━ [37m━━━━ 1:10 263毫秒/步 - 损失: 1.0259 - 平均绝对误差: 0.8100

1331/1599 ━━━━━━━━━━━━━━━━ [37m━━━━ 1:10 263毫秒/步 - 损失: 1.0259 - 平均绝对误差: 0.8100

1332/1599 ━━━━━━━━━━━━━━━━ [37m━━━━ 1:10 263毫秒/步 - 损失: 1.0259 - 平均绝对误差: 0.8100

1333/1599 ━━━━━━━━━━━━━━━━ [37m━━━━ 1:09 263毫秒/步 - 损失: 1.0258 - 平均绝对误差: 0.8100

1334/1599 ━━━━━━━━━━━━━━━━ [37m━━━━ 1:09 263毫秒/步 - 损失: 1.0258 - 平均绝对误差: 0.8100

1335/1599 ━━━━━━━━━━━━━━━━ [37m━━━━ 1:09 263毫秒/步 - 损失: 1.0258 - 平均绝对误差: 0.8100

1336/1599 ━━━━━━━━━━━━━━━━ [37m━━━━ 1:09 263毫秒/步 - 损失: 1.0258 - 平均绝对误差: 0.8099

1337/1599 ━━━━━━━━━━━━━━━━ [37m━━━━ 1:08 263毫秒/步 - 损失: 1.0258 - 平均绝对误差: 0.8099

1338/1599 ━━━━━━━━━━━━━━━━ [37m━━━━ 1:08 263毫秒/步 - 损失: 1.0258 - 平均绝对误差: 0.8099

1339/1599 ━━━━━━━━━━━━━━━━ [37m━━━━ 1:08 263毫秒/步 - 损失: 1.0257 - 平均绝对误差: 0.8099

1340/1599 ━━━━━━━━━━━━━━━━ [37m━━━━ 1:08 263毫秒/步 - 损失: 1.0257 - 平均绝对误差: 0.8099

1341/1599 ━━━━━━━━━━━━━━━━ [37m━━━━ 1:07 263毫秒/步 - 损失: 1.0257 - 平均绝对误差: 0.8099

1342/1599 ━━━━━━━━━━━━━━━━ [37m━━━━ 1:07 263毫秒/步 - 损失: 1.0257 - 平均绝对误差: 0.8099

1343/1599 ━━━━━━━━━━━━━━━━ [37m━━━━ 1:07 263毫秒/步 - 损失: 1.0257 - 平均绝对误差: 0.8099

1344/1599 ━━━━━━━━━━━━━━━━ [37m━━━━ 1:06 263毫秒/步 - 损失: 1.0257 - 平均绝对误差: 0.8099

1345/1599 ━━━━━━━━━━━━━━━━ [37m━━━━ 1:06 263毫秒/步 - 损失: 1.0257 - 平均绝对误差: 0.8099

1346/1599 ━━━━━━━━━━━━━━━━ [37m━━━━ 1:06 263毫秒/步 - 损失: 1.0256 - 平均绝对误差: 0.8099

1347/1599 ━━━━━━━━━━━━━━━━ [37m━━━━ 1:06 263毫秒/步 - 损失: 1.0256 - 平均绝对误差: 0.8099

1348/1599 ━━━━━━━━━━━━━━━━ [37m━━━━ 1:05 263毫秒/步 - 损失: 1.0256 - 平均绝对误差: 0.8099

1349/1599 ━━━━━━━━━━━━━━━━ [37m━━━━ 1:05 263毫秒/步 - 损失: 1.0256 - 平均绝对误差: 0.8099

1350/1599 ━━━━━━━━━━━━━━━━ [37m━━━━ 1:05 263毫秒/步 - 损失: 1.0256 - 平均绝对误差: 0.8099

1351/1599 ━━━━━━━━━━━━━━━━ [37m━━━━ 1:05 263毫秒/步 - 损失: 1.0256 - 平均绝对误差: 0.8098

1352/1599 ━━━━━━━━━━━━━━━━ [37m━━━━ 1:04 263毫秒/步 - 损失: 1.0256 - 平均绝对误差: 0.8098

1353/1599 ━━━━━━━━━━━━━━━━ [37m━━━━ 1:04 263毫秒/步 - 损失: 1.0255 - 平均绝对误差: 0.8098

1354/1599 ━━━━━━━━━━━━━━━━ [37m━━━━ 1:04 263毫秒/步 - 损失: 1.0255 - 平均绝对误差: 0.8098

1355/1599 ━━━━━━━━━━━━━━━━ [37m━━━━ 1:04 263毫秒/步 - 损失: 1.0255 - 平均绝对误差: 0.8098

1356/1599 ━━━━━━━━━━━━━━━━ [37m━━━━ 1:03 263毫秒/步 - 损失: 1.0255 - 平均绝对误差: 0.8098

1357/1599 ━━━━━━━━━━━━━━━━ [37m━━━━ 1:03 263毫秒/步 - 损失: 1.0255 - 平均绝对误差: 0.8098

1358/1599 ━━━━━━━━━━━━━━━━ [37m━━━━ 1:03 263毫秒/步 - 损失: 1.0255 - 平均绝对误差: 0.8098

1359/1599 ━━━━━━━━━━━━━━━━ [37m━━━━ 1:03 263毫秒/步 - 损失: 1.0255 - 平均绝对误差: 0.8098

1360/1599 ━━━━━━━━━━━━━━━━━ [37m━━━ 1:02 263毫秒/步 - 损失: 1.0254 - 平均绝对误差: 0.8098

1361/1599 ━━━━━━━━━━━━━━━━━ [37m━━━ 1:02 263毫秒/步 - 损失: 1.0254 - 平均绝对误差: 0.8098

1362/1599 ━━━━━━━━━━━━━━━━━ [37m━━━ 1:02 263毫秒/步 - 损失: 1.0254 - 平均绝对误差: 0.8098

1363/1599 ━━━━━━━━━━━━━━━━━ [37m━━━ 1:02 263毫秒/步 - 损失: 1.0254 - 平均绝对误差: 0.8098

1364/1599 ━━━━━━━━━━━━━━━━━ [37m━━━ 1:01 263毫秒/步 - 损失: 1.0254 - 平均绝对误差: 0.8098

1365/1599 ━━━━━━━━━━━━━━━━━ [37m━━━ 1:01 263毫秒/步 - 损失: 1.0254 - 平均绝对误差: 0.8098

1366/1599 ━━━━━━━━━━━━━━━━━ [37m━━━ 1:01 263毫秒/步 - 损失: 1.0254 - 平均绝对误差: 0.8097

1367/1599 ━━━━━━━━━━━━━━━━━ [37m━━━ 1:01 263毫秒/步 - 损失: 1.0253 - 平均绝对误差: 0.8097

1368/1599 ━━━━━━━━━━━━━━━━━ [37m━━━ 1:00 263毫秒/步 - 损失: 1.0253 - 平均绝对误差: 0.8097

1369/1599 ━━━━━━━━━━━━━━━━━ [37m━━━ 1:00 263毫秒/步 - 损失: 1.0253 - 平均绝对误差: 0.8097

1370/1599 ━━━━━━━━━━━━━━━━━ [37m━━━ 1:00 263毫秒/步 - 损失: 1.0253 - 平均绝对误差: 0.8097

1371/1599 ━━━━━━━━━━━━━━━━━ [37m━━━ 1:00 263毫秒/步 - 损失: 1.0253 - 平均绝对误差: 0.8097

1372/1599 ━━━━━━━━━━━━━━━━━ [37m━━━ 59秒 263毫秒/步 - 损失: 1.0253 - 平均绝对误差: 0.8097

1373/1599 ━━━━━━━━━━━━━━━━━ [37m━━━ 59秒 263毫秒/步 - 损失: 1.0253 - 平均绝对误差: 0.8097

1374/1599 ━━━━━━━━━━━━━━━━━ [37m━━━ 59秒 263毫秒/步 - 损失: 1.0253 - 平均绝对误差: 0.8097

1375/1599 ━━━━━━━━━━━━━━━━━ [37m━━━ 58秒 263毫秒/步 - 损失: 1.0252 - 平均绝对误差: 0.8097

1376/1599 ━━━━━━━━━━━━━━━━━ [37m━━━ 58秒 263毫秒/步 - 损失: 1.0252 - 平均绝对误差: 0.8097

1377/1599 ━━━━━━━━━━━━━━━━━ [37m━━━ 58秒 263毫秒/步 - 损失: 1.0252 - 平均绝对误差: 0.8097

1378/1599 ━━━━━━━━━━━━━━━━━ [37m━━━ 58秒 263毫秒/步 - 损失: 1.0252 - 平均绝对误差: 0.8097

1379/1599 ━━━━━━━━━━━━━━━━━ [37m━━━ 57秒 263毫秒/步 - 损失: 1.0252 - 平均绝对误差: 0.8097

1380/1599 ━━━━━━━━━━━━━━━━━ [37m━━━ 57秒 263毫秒/步 - 损失: 1.0252 - 平均绝对误差: 0.8097

1381/1599 ━━━━━━━━━━━━━━━━━ [37m━━━ 57秒 263毫秒/步 - 损失: 1.0252 - 平均绝对误差: 0.8097

1382/1599 ━━━━━━━━━━━━━━━━━ [37m━━━ 57秒 263毫秒/步 - 损失: 1.0251 - 平均绝对误差: 0.8096

1383/1599 ━━━━━━━━━━━━━━━━━ [37m━━━ 56秒 263毫秒/步 - 损失: 1.0251 - 平均绝对误差: 0.8096

1384/1599 ━━━━━━━━━━━━━━━━━ [37m━━━ 56秒 263毫秒/步 - 损失: 1.0251 - 平均绝对误差: 0.8096

1385/1599 ━━━━━━━━━━━━━━━━━ [37m━━━ 56秒 263毫秒/步 - 损失: 1.0251 - 平均绝对误差: 0.8096

1386/1599 ━━━━━━━━━━━━━━━━━ [37m━━━ 56秒 263毫秒/步 - 损失: 1.0251 - 平均绝对误差: 0.8096

1387/1599 ━━━━━━━━━━━━━━━━━ [37m━━━ 55秒 263毫秒/步 - 损失: 1.0251 - 平均绝对误差: 0.8096

1388/1599 ━━━━━━━━━━━━━━━━━ [37m━━━ 55秒 263毫秒/步 - 损失: 1.0251 - 平均绝对误差: 0.8096

1389/1599 ━━━━━━━━━━━━━━━━━ [37m━━━ 55秒 263毫秒/步 - 损失: 1.0251 - 平均绝对误差: 0.8096

1390/1599 ━━━━━━━━━━━━━━━━━ [37m━━━ 55秒 263毫秒/步 - 损失: 1.0250 - 平均绝对误差: 0.8096

1391/1599 ━━━━━━━━━━━━━━━━━ [37m━━━ 54秒 263毫秒/步 - 损失: 1.0250 - 平均绝对误差: 0.8096

1392/1599 ━━━━━━━━━━━━━━━━━ [37m━━━ 54秒 263毫秒/步 - 损失: 1.0250 - 平均绝对误差: 0.8096

1393/1599 ━━━━━━━━━━━━━━━━━ [37m━━━ 54秒 264毫秒/步 - 损失: 1.0250 - 平均绝对误差: 0.8096

1394/1599 ━━━━━━━━━━━━━━━━━ [37m━━━ 54秒 264毫秒/步 - 损失: 1.0250 - 平均绝对误差: 0.8096

1395/1599 ━━━━━━━━━━━━━━━━━ [37m━━━ 53秒 264毫秒/步 - 损失: 1.0250 - 平均绝对误差: 0.8096

1396/1599 ━━━━━━━━━━━━━━━━━ [37m━━━ 53秒 264毫秒/步 - 损失: 1.0250 - 平均绝对误差: 0.8096

1397/1599 ━━━━━━━━━━━━━━━━━ [37m━━━ 53秒 264毫秒/步 - 损失: 1.0250 - 平均绝对误差: 0.8096

1398/1599 ━━━━━━━━━━━━━━━━━ [37m━━━ 52秒 264毫秒/步 - 损失: 1.0249 - 平均绝对误差: 0.8095

1399/1599 ━━━━━━━━━━━━━━━━━ [37m━━━ 52秒 264毫秒/步 - 损失: 1.0249 - 平均绝对误差: 0.8095

1400/1599 ━━━━━━━━━━━━━━━━━ [37m━━━ 52秒 264毫秒/步 - 损失: 1.0249 - 平均绝对误差: 0.8095

1401/1599 ━━━━━━━━━━━━━━━━━ [37m━━━ 52秒 264毫秒/步 - 损失: 1.0249 - 平均绝对误差: 0.8095

1402/1599 ━━━━━━━━━━━━━━━━━ [37m━━━ 51秒 264毫秒/步 - 损失: 1.0249 - 平均绝对误差: 0.8095

1403/1599 ━━━━━━━━━━━━━━━━━ [37m━━━ 51秒 264毫秒/步 - 损失: 1.0249 - 平均绝对误差: 0.8095

1404/1599 ━━━━━━━━━━━━━━━━━ [37m━━━ 51秒 264毫秒/步 - 损失: 1.0249 - 平均绝对误差: 0.8095

1405/1599 ━━━━━━━━━━━━━━━━━ [37m━━━ 51秒 264毫秒/步 - 损失: 1.0248 - 平均绝对误差: 0.8095

1406/1599 ━━━━━━━━━━━━━━━━━ [37m━━━ 50秒 264毫秒/步 - 损失: 1.0248 - 平均绝对误差: 0.8095

1407/1599 ━━━━━━━━━━━━━━━━━ [37m━━━ 50秒 264毫秒/步 - 损失: 1.0248 - 平均绝对误差: 0.8095

1408/1599 ━━━━━━━━━━━━━━━━━ [37m━━━ 50秒 264毫秒/步 - 损失: 1.0248 - 平均绝对误差: 0.8095

1409/1599 ━━━━━━━━━━━━━━━━━ [37m━━━ 50秒 264毫秒/步 - 损失: 1.0248 - 平均绝对误差: 0.8095

1410/1599 ━━━━━━━━━━━━━━━━━ [37m━━━ 49秒 264毫秒/步 - 损失: 1.0248 - 平均绝对误差: 0.8095

1411/1599 ━━━━━━━━━━━━━━━━━ [37m━━━ 49秒 264毫秒/步 - 损失: 1.0248 - 平均绝对误差: 0.8095

1412/1599 ━━━━━━━━━━━━━━━━━ [37m━━━ 49秒 264毫秒/步 - 损失: 1.0248 - 平均绝对误差: 0.8095

1413/1599 ━━━━━━━━━━━━━━━━━ [37m━━━ 49秒 264毫秒/步 - 损失: 1.0247 - 平均绝对误差: 0.8095

1414/1599 ━━━━━━━━━━━━━━━━━ [37m━━━ 48秒 264毫秒/步 - 损失: 1.0247 - 平均绝对误差: 0.8094

1415/1599 ━━━━━━━━━━━━━━━━━ [37m━━━ 48秒 264毫秒/步 - 损失: 1.0247 - 平均绝对误差: 0.8094

1416/1599 ━━━━━━━━━━━━━━━━━ [37m━━━ 48秒 264毫秒/步 - 损失: 1.0247 - 平均绝对误差: 0.8094

1417/1599 ━━━━━━━━━━━━━━━━━ [37m━━━ 47秒 264毫秒/步 - 损失: 1.0247 - 平均绝对误差: 0.8094

1418/1599 ━━━━━━━━━━━━━━━━━ [37m━━━ 47秒 264毫秒/步 - 损失: 1.0247 - 平均绝对误差: 0.8094

1419/1599 ━━━━━━━━━━━━━━━━━ [37m━━━ 47秒 264毫秒/步 - 损失: 1.0247 - 平均绝对误差: 0.8094

1420/1599 ━━━━━━━━━━━━━━━━━ [37m━━━ 47秒 264毫秒/步 - 损失: 1.0247 - 平均绝对误差: 0.8094

1421/1599 ━━━━━━━━━━━━━━━━━ [37m━━━ 46秒 264毫秒/步 - 损失: 1.0246 - 平均绝对误差: 0.8094

1422/1599 ━━━━━━━━━━━━━━━━━ [37m━━━ 46秒 264毫秒/步 - 损失: 1.0246 - 平均绝对误差: 0.8094

1423/1599 ━━━━━━━━━━━━━━━━━ [37m━━━ 46秒 264毫秒/步 - 损失: 1.0246 - 平均绝对误差: 0.8094

1424/1599 ━━━━━━━━━━━━━━━━━ [37m━━━ 46秒 264毫秒/步 - 损失: 1.0246 - 平均绝对误差: 0.8094

1425/1599 ━━━━━━━━━━━━━━━━━ [37m━━━ 45秒 264毫秒/步 - 损失: 1.0246 - 平均绝对误差: 0.8094

1426/1599 ━━━━━━━━━━━━━━━━━ [37m━━━ 45秒 264毫秒/步 - 损失: 1.0246 - 平均绝对误差: 0.8094

1427/1599 ━━━━━━━━━━━━━━━━━ [37m━━━ 45秒 264毫秒/步 - 损失: 1.0246 - 平均绝对误差: 0.8094

1428/1599 ━━━━━━━━━━━━━━━━━ [37m━━━ 45秒 264毫秒/步 - 损失: 1.0246 - 平均绝对误差: 0.8094

1429/1599 ━━━━━━━━━━━━━━━━━ [37m━━━ 44秒 264毫秒/步 - 损失: 1.0245 - 平均绝对误差: 0.8094

1430/1599 ━━━━━━━━━━━━━━━━━ [37m━━━ 44秒 263毫秒/步 - 损失: 1.0245 - 平均绝对误差: 0.8094

1431/1599 ━━━━━━━━━━━━━━━━━ [37m━━━ 44秒 263毫秒/步 - 损失: 1.0245 - 平均绝对误差: 0.8093

1432/1599 ━━━━━━━━━━━━━━━━━ [37m━━━ 43秒 263毫秒/步 - 损失: 1.0245 - 平均绝对误差: 0.8093

1433/1599 ━━━━━━━━━━━━━━━━━ [37m━━━ 43秒 263毫秒/步 - 损失: 1.0245 - 平均绝对误差: 0.8093

1434/1599 ━━━━━━━━━━━━━━━━━ [37m━━━ 43秒 263毫秒/步 - 损失: 1.0245 - 平均绝对误差: 0.8093

1435/1599 ━━━━━━━━━━━━━━━━━ [37m━━━ 43秒 263毫秒/步 - 损失: 1.0245 - 平均绝对误差: 0.8093

1436/1599 ━━━━━━━━━━━━━━━━━ [37m━━━ 42秒 263毫秒/步 - 损失: 1.0245 - 平均绝对误差: 0.8093

1437/1599 ━━━━━━━━━━━━━━━━━ [37m━━━ 42秒 263毫秒/步 - 损失: 1.0244 - 平均绝对误差: 0.8093

1438/1599 ━━━━━━━━━━━━━━━━━ [37m━━━ 42秒 263毫秒/步 - 损失: 1.0244 - 平均绝对误差: 0.8093

1439/1599 ━━━━━━━━━━━━━━━━━ [37m━━━ 42秒 263毫秒/步 - 损失: 1.0244 - 平均绝对误差: 0.8093

1440/1599 ━━━━━━━━━━━━━━━━━━ [37m━━ 41秒 263毫秒/步 - 损失: 1.0244 - 平均绝对误差: 0.8093

1441/1599 ━━━━━━━━━━━━━━━━━━ [37m━━ 41秒 264毫秒/步 - 损失: 1.0244 - 平均绝对误差: 0.8093

1442/1599 ━━━━━━━━━━━━━━━━━━ [37m━━ 41秒 264毫秒/步 - 损失: 1.0244 - 平均绝对误差: 0.8093

1443/1599 ━━━━━━━━━━━━━━━━━━ [37m━━ 41秒 264毫秒/步 - 损失: 1.0244 - 平均绝对误差: 0.8093

1444/1599 ━━━━━━━━━━━━━━━━━━ [37m━━ 40s 264ms/step - loss: 1.0244 - 平均绝对误差: 0.8093

1445/1599 ━━━━━━━━━━━━━━━━━━ [37m━━ 40s 264ms/step - loss: 1.0243 - 平均绝对误差: 0.8093

1446/1599 ━━━━━━━━━━━━━━━━━━ [37m━━ 40s 264ms/step - loss: 1.0243 - 平均绝对误差: 0.8093

1447/1599 ━━━━━━━━━━━━━━━━━━ [37m━━ 40s 264ms/step - loss: 1.0243 - 平均绝对误差: 0.8093

1448/1599 ━━━━━━━━━━━━━━━━━━ [37m━━ 39s 264ms/step - loss: 1.0243 - 平均绝对误差: 0.8092

1449/1599 ━━━━━━━━━━━━━━━━━━ [37m━━ 39s 264ms/step - loss: 1.0243 - 平均绝对误差: 0.8092

1450/1599 ━━━━━━━━━━━━━━━━━━ [37m━━ 39s 264ms/step - loss: 1.0243 - 平均绝对误差: 0.8092

1451/1599 ━━━━━━━━━━━━━━━━━━ [37m━━ 39s 264ms/step - loss: 1.0243 - 平均绝对误差: 0.8092

1452/1599 ━━━━━━━━━━━━━━━━━━ [37m━━ 38s 264ms/step - loss: 1.0243 - 平均绝对误差: 0.8092

1453/1599 ━━━━━━━━━━━━━━━━━━ [37m━━ 38s 264ms/step - loss: 1.0243 - 平均绝对误差: 0.8092

1454/1599 ━━━━━━━━━━━━━━━━━━ [37m━━ 38s 264ms/step - loss: 1.0242 - 平均绝对误差: 0.8092

1455/1599 ━━━━━━━━━━━━━━━━━━ [37m━━ 37s 264ms/step - loss: 1.0242 - 平均绝对误差: 0.8092

1456/1599 ━━━━━━━━━━━━━━━━━━ [37m━━ 37s 264ms/step - loss: 1.0242 - 平均绝对误差: 0.8092

1457/1599 ━━━━━━━━━━━━━━━━━━ [37m━━ 37s 264ms/step - loss: 1.0242 - 平均绝对误差: 0.8092

1458/1599 ━━━━━━━━━━━━━━━━━━ [37m━━ 37s 264ms/step - loss: 1.0242 - 平均绝对误差: 0.8092

1459/1599 ━━━━━━━━━━━━━━━━━━ [37m━━ 36s 264ms/step - loss: 1.0242 - 平均绝对误差: 0.8092

1460/1599 ━━━━━━━━━━━━━━━━━━ [37m━━ 36s 264ms/step - loss: 1.0242 - 平均绝对误差: 0.8092

1461/1599 ━━━━━━━━━━━━━━━━━━ [37m━━ 36s 264ms/step - loss: 1.0242 - 平均绝对误差: 0.8092

1462/1599 ━━━━━━━━━━━━━━━━━━ [37m━━ 36s 264ms/step - loss: 1.0241 - 平均绝对误差: 0.8092

1463/1599 ━━━━━━━━━━━━━━━━━━ [37m━━ 35s 264ms/step - loss: 1.0241 - 平均绝对误差: 0.8092

1464/1599 ━━━━━━━━━━━━━━━━━━ [37m━━ 35s 264ms/step - loss: 1.0241 - 平均绝对误差: 0.8092

1465/1599 ━━━━━━━━━━━━━━━━━━ [37m━━ 35s 264ms/step - loss: 1.0241 - 平均绝对误差: 0.8092

1466/1599 ━━━━━━━━━━━━━━━━━━ [37m━━ 35s 264ms/step - loss: 1.0241 - 平均绝对误差: 0.8091

1467/1599 ━━━━━━━━━━━━━━━━━━ [37m━━ 34s 264ms/step - loss: 1.0241 - 平均绝对误差: 0.8091

1468/1599 ━━━━━━━━━━━━━━━━━━ [37m━━ 34s 264ms/step - loss: 1.0241 - 平均绝对误差: 0.8091

1469/1599 ━━━━━━━━━━━━━━━━━━ [37m━━ 34s 264ms/step - loss: 1.0241 - 平均绝对误差: 0.8091

1470/1599 ━━━━━━━━━━━━━━━━━━ [37m━━ 34s 264ms/step - loss: 1.0241 - 平均绝对误差: 0.8091

1471/1599 ━━━━━━━━━━━━━━━━━━ [37m━━ 33s 264ms/step - loss: 1.0240 - 平均绝对误差: 0.8091

1472/1599 ━━━━━━━━━━━━━━━━━━ [37m━━ 33s 264ms/step - loss: 1.0240 - 平均绝对误差: 0.8091

1473/1599 ━━━━━━━━━━━━━━━━━━ [37m━━ 33s 264ms/step - loss: 1.0240 - 平均绝对误差: 0.8091

1474/1599 ━━━━━━━━━━━━━━━━━━ [37m━━ 32s 264ms/step - loss: 1.0240 - 平均绝对误差: 0.8091

1475/1599 ━━━━━━━━━━━━━━━━━━ [37m━━ 32s 264ms/step - loss: 1.0240 - 平均绝对误差: 0.8091

1476/1599 ━━━━━━━━━━━━━━━━━━ [37m━━ 32s 264ms/step - loss: 1.0240 - 平均绝对误差: 0.8091

1477/1599 ━━━━━━━━━━━━━━━━━━ [37m━━ 32s 264ms/step - loss: 1.0240 - 平均绝对误差: 0.8091

1478/1599 ━━━━━━━━━━━━━━━━━━ [37m━━ 31s 264ms/step - loss: 1.0240 - 平均绝对误差: 0.8091

1479/1599 ━━━━━━━━━━━━━━━━━━ [37m━━ 31s 264ms/step - loss: 1.0239 - 平均绝对误差: 0.8091

1480/1599 ━━━━━━━━━━━━━━━━━━ [37m━━ 31s 264ms/step - loss: 1.0239 - 平均绝对误差: 0.8091

1481/1599 ━━━━━━━━━━━━━━━━━━ [37m━━ 31s 264ms/step - loss: 1.0239 - 平均绝对误差: 0.8091

1482/1599 ━━━━━━━━━━━━━━━━━━ [37m━━ 30s 264ms/step - loss: 1.0239 - 平均绝对误差: 0.8091

1483/1599 ━━━━━━━━━━━━━━━━━━ [37m━━ 30s 264ms/step - loss: 1.0239 - 平均绝对误差: 0.8091

1484/1599 ━━━━━━━━━━━━━━━━━━ [37m━━ 30s 264ms/step - loss: 1.0239 - 平均绝对误差: 0.8090

1485/1599 ━━━━━━━━━━━━━━━━━━ [37m━━ 30s 264ms/step - loss: 1.0239 - 平均绝对误差: 0.8090

1486/1599 ━━━━━━━━━━━━━━━━━━ [37m━━ 29s 264ms/step - loss: 1.0239 - 平均绝对误差: 0.8090

1487/1599 ━━━━━━━━━━━━━━━━━━ [37m━━ 29s 264ms/step - loss: 1.0239 - 平均绝对误差: 0.8090

1488/1599 ━━━━━━━━━━━━━━━━━━ [37m━━ 29s 264ms/step - loss: 1.0238 - 平均绝对误差: 0.8090

1489/1599 ━━━━━━━━━━━━━━━━━━ [37m━━ 29s 264ms/step - loss: 1.0238 - 平均绝对误差: 0.8090

1490/1599 ━━━━━━━━━━━━━━━━━━ [37m━━ 28s 264ms/step - loss: 1.0238 - 平均绝对误差: 0.8090

1491/1599 ━━━━━━━━━━━━━━━━━━ [37m━━ 28s 264ms/step - loss: 1.0238 - 平均绝对误差: 0.8090

1492/1599 ━━━━━━━━━━━━━━━━━━ [37m━━ 28s 264ms/step - loss: 1.0238 - 平均绝对误差: 0.8090

1493/1599 ━━━━━━━━━━━━━━━━━━ [37m━━ 27s 264ms/step - loss: 1.0238 - 平均绝对误差: 0.8090

1494/1599 ━━━━━━━━━━━━━━━━━━ [37m━━ 27s 264ms/step - loss: 1.0238 - 平均绝对误差: 0.8090

1495/1599 ━━━━━━━━━━━━━━━━━━ [37m━━ 27s 264ms/step - loss: 1.0238 - 平均绝对误差: 0.8090

1496/1599 ━━━━━━━━━━━━━━━━━━ [37m━━ 27s 264ms/step - loss: 1.0237 - 平均绝对误差: 0.8090

1497/1599 ━━━━━━━━━━━━━━━━━━ [37m━━ 26s 264ms/step - loss: 1.0237 - 平均绝对误差: 0.8090

1498/1599 ━━━━━━━━━━━━━━━━━━ [37m━━ 26s 264ms/step - loss: 1.0237 - 平均绝对误差: 0.8090

1499/1599 ━━━━━━━━━━━━━━━━━━ [37m━━ 26s 264ms/step - loss: 1.0237 - 平均绝对误差: 0.8090

1500/1599 ━━━━━━━━━━━━━━━━━━ [37m━━ 26s 264ms/step - loss: 1.0237 - 平均绝对误差: 0.8090

1501/1599 ━━━━━━━━━━━━━━━━━━ [37m━━ 25s 264ms/step - loss: 1.0237 - 平均绝对误差: 0.8090

1502/1599 ━━━━━━━━━━━━━━━━━━ [37m━━ 25s 264ms/step - loss: 1.0237 - 平均绝对误差: 0.8089

1503/1599 ━━━━━━━━━━━━━━━━━━ [37m━━ 25s 264ms/step - loss: 1.0237 - 平均绝对误差: 0.8089

1504/1599 ━━━━━━━━━━━━━━━━━━ [37m━━ 25s 264ms/step - loss: 1.0236 - 平均绝对误差: 0.8089

1505/1599 ━━━━━━━━━━━━━━━━━━ [37m━━ 24s 264ms/step - loss: 1.0236 - 平均绝对误差: 0.8089

1506/1599 ━━━━━━━━━━━━━━━━━━ [37m━━ 24s 264ms/step - loss: 1.0236 - 平均绝对误差: 0.8089

1507/1599 ━━━━━━━━━━━━━━━━━━ [37m━━ 24s 264ms/step - loss: 1.0236 - 平均绝对误差: 0.8089

1508/1599 ━━━━━━━━━━━━━━━━━━ [37m━━ 24s 264ms/step - loss: 1.0236 - 平均绝对误差: 0.8089

1509/1599 ━━━━━━━━━━━━━━━━━━ [37m━━ 23s 264ms/step - loss: 1.0236 - 平均绝对误差: 0.8089

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1547/1599 ━━━━━━━━━━━━━━━━━━━ [37m━ 13s 265ms/step - loss: 1.0231 - 平均绝对误差: 0.8087

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1550/1599 ━━━━━━━━━━━━━━━━━━━ [37m━ 12s 265ms/step - loss: 1.0231 - 平均绝对误差: 0.8087

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1554/1599 ━━━━━━━━━━━━━━━━━━━ [37m━ 11s 265ms/step - loss: 1.0231 - 平均绝对误差: 0.8087

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1556/1599 ━━━━━━━━━━━━━━━━━━━ [37m━ 11s 265ms/step - loss: 1.0230 - 平均绝对误差: 0.8087

1557/1599 ━━━━━━━━━━━━━━━━━━━ [37m━ 11s 265ms/step - loss: 1.0230 - 平均绝对误差: 0.8086

1558/1599 ━━━━━━━━━━━━━━━━━━━ [37m━ 10s 265ms/step - loss: 1.0230 - 平均绝对误差: 0.8086

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1564/1599 ━━━━━━━━━━━━━━━━━━━ [37m━ 9s 265ms/step - loss: 1.0230 - 平均绝对误差: 0.8086

1565/1599 ━━━━━━━━━━━━━━━━━━━ [37m━ 8s 265ms/step - loss: 1.0229 - 平均绝对误差: 0.8086

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1581/1599 ━━━━━━━━━━━━━━━━━━━ [37m━ 4s 265ms/step - loss: 1.0228 - 平均绝对误差: 0.8085

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1592/1599 ━━━━━━━━━━━━━━━━━━━ [37m━ 1s 265ms/step - loss: 1.0226 - 平均绝对误差: 0.8085

1593/1599 ━━━━━━━━━━━━━━━━━━━ [37m━ 1s 265ms/step - loss: 1.0226 - 平均绝对误差: 0.8085

1594/1599 ━━━━━━━━━━━━━━━━━━━ [37m━ 1s 265ms/step - loss: 1.0226 - 平均绝对误差: 0.8084

1595/1599 ━━━━━━━━━━━━━━━━━━━ [37m━ 1s 265ms/step - loss: 1.0226 - 平均绝对误差: 0.8084

1596/1599 ━━━━━━━━━━━━━━━━━━━ [37m━ 0s 265ms/step - loss: 1.0226 - 平均绝对误差: 0.8084

1597/1599 ━━━━━━━━━━━━━━━━━━━ [37m━ 0s 265ms/step - loss: 1.0226 - 平均绝对误差: 0.8084

1598/1599 ━━━━━━━━━━━━━━━━━━━ [37m━ 0s 265ms/step - loss: 1.0226 - 平均绝对误差: 0.8084

1599/1599 ━━━━━━━━━━━━━━━━━━━━ 0s 265ms/step - loss: 1.0226 - 平均绝对误差: 0.8084

1599/1599 ━━━━━━━━━━━━━━━━━━━━ 424s 265ms/step - loss: 1.0226 - 平均绝对误差: 0.8084

Test MAE: 0.779

/home/humbulani/tensorflow-env/env/lib/python3.11/site-packages/keras/src/trainers/epoch_iterator.py:159: UserWarning: Your input ran out of data; interrupting training. Make sure that your dataset or generator can generate at least `steps_per_epoch * epochs` batches. You may need to use the `.repeat()` function when building your dataset.
  self._interrupted_warning()

你应该在测试数据上达到或接近 0.7 的平均绝对误差 (MAE)。

结论

BST 模型在其架构中使用了 Transformer 层，以捕捉用户行为序列中的顺序信号用于推荐。

你可以尝试使用不同的配置来训练此模型，例如通过增加输入序列长度和训练更多的 epoch。此外，你还可以尝试加入其他特征，如电影上映年份和顾客邮政编码，以及加入交叉特征，如性别 X 类型。