我有一个重塑输入错误，我不知道为什么。请求的形状是1058400，什么是（1,21168）乘以50的批量大小。我不明白的是677376的表观输入大小。我不知道这个值来自何处。重塑前的图层是展平图层，当我定义Reshape图层的目标形状时，我直接使用它的形状。
模型编译得很好，我使用Tensorflow作为后端，因此它在运行时之前定义。但只有当我把日期放入其中时才会出现错误。
码：
import numpy as np
import tensorflow as tf
import keras.backend as K
from keras import Model
from keras.layers import LSTM, Conv2D, Dense, Flatten, Input, Reshape
from keras.optimizers import Adam
config = tf.ConfigProto(allow_soft_placement=True)
sess = tf.Session(config=config)
K.set_session(sess)
input = Input(batch_shape=(50, 230, 230, 1))
conv1 = Conv2D(
filters=12, kernel_size=(7, 7), strides=(1, 1), padding="valid", activation="relu"
)(input)
conv2 = Conv2D(
filters=24, kernel_size=(5, 5), strides=(1, 1), padding="valid", activation="relu"
)(conv1)
conv3 = Conv2D(
filters=48, kernel_size=(3, 3), strides=(2, 2), padding="valid", activation="relu"
)(conv2)
conv4 = Conv2D(
filters=48, kernel_size=(5, 5), strides=(5, 5), padding="valid", activation="relu"
)(conv3)
conv_out = Flatten()(conv4)
conv_out = Reshape(target_shape=(1, int(conv_out.shape[1])))(conv_out)
conv_out = Dense(128, activation="relu")(conv_out)
rnn_1 = LSTM(128, stateful=True, return_sequences=True)(conv_out)
rnn_2 = LSTM(128, stateful=True, return_sequences=True)(rnn_1)
rnn_3 = LSTM(128, stateful=True, return_sequences=False)(rnn_2)
value = Dense(1, activation="linear")(rnn_3)
policy = Dense(5, activation="softmax")(rnn_3)
model = Model(inputs=input, outputs=[value, policy])
adam = Adam(lr=0.001)
model.compile(loss="mse", optimizer=adam)
model.summary()
out = model.predict(np.random.randint(1, 5, size=(50, 230, 230, 1)))
print(out)
摘要：
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
input_1 (InputLayer) (50, 230, 230, 1) 0
__________________________________________________________________________________________________
conv2d (Conv2D) (50, 224, 224, 12) 600 input_1[0][0]
__________________________________________________________________________________________________
conv2d_1 (Conv2D) (50, 220, 220, 24) 7224 conv2d[0][0]
__________________________________________________________________________________________________
conv2d_2 (Conv2D) (50, 109, 109, 48) 10416 conv2d_1[0][0]
__________________________________________________________________________________________________
conv2d_3 (Conv2D) (50, 21, 21, 48) 57648 conv2d_2[0][0]
__________________________________________________________________________________________________
flatten (Flatten) (50, 21168) 0 conv2d_3[0][0]
__________________________________________________________________________________________________
reshape (Reshape) (50, 1, 21168) 0 flatten[0][0]
__________________________________________________________________________________________________
dense (Dense) (50, 1, 128) 2709632 reshape[0][0]
__________________________________________________________________________________________________
lstm (LSTM) (50, 1, 128) 131584 dense[0][0]
__________________________________________________________________________________________________
lstm_1 (LSTM) (50, 1, 128) 131584 lstm[0][0]
__________________________________________________________________________________________________
lstm_2 (LSTM) (50, 128) 131584 lstm_1[0][0]
__________________________________________________________________________________________________
dense_1 (Dense) (50, 1) 129 lstm_2[0][0]
__________________________________________________________________________________________________
dense_2 (Dense) (50, 5) 645 lstm_2[0][0]
==================================================================================================
Total params: 3,181,046
Trainable params: 3,181,046
Non-trainable params: 0
编辑：
上述代码出错：
Traceback (most recent call last):
File "foo.py", line 45, in <module>
out = model.predict(np.random.randint(1, 5, size=(50, 230, 230, 1)))
File "/home/vyz/.conda/envs/stackoverflow/lib/python3.6/site-packages/keras/engine/training.py", line 1157, in predict
'Batch size: ' + str(batch_size) + '.')
ValueError: In a stateful network, you should only pass inputs with a number of samples that can be divided by the batch size. Found: 50 samples. Batch size: 32.
重要提示：我已编辑了您的问题，因此它实际运行并代表您的问题。 Input应该采取目前提供的batch_shape。下次请确保您的代码有效，将更容易。
解决方案很简单;传递给网络的批次具有错误的维度。
677376 / 21168 = 32它是predict预期的批次的默认大小。你应该指定它，如果它不同（在你的情况下是50），像这样：
out = model.predict(np.random.randint(1, 5, size=(50, 230, 230, 1)), batch_size=50)
现在一切都应该正常工作，如果你想要硬编码，请记得指定批量大小。