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我有一个重塑输入错误，我不知道为什么。请求的形状是 1058400，等于 (1, 21168) 乘以批量大小 50。我不明白的是 677376 的表观输入大小。我不知道这个值是从哪里来的。reshape 之前的图层是 flatten 图层，我在定义 Reshape 图层的目标形状时直接使用它的形状。

模型编译得很好，我使用 Tensorflow 作为后端，所以它是在运行时之前定义的。但是只有当我输入日期时才会出现错误。

代码：

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)

概括：

__________________________________________________________________________________________________
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：

上述代码的错误：

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.
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1 回答 1

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问题版

重要提示：我已经编辑了您的问题，因此它实际运行并代表了您的问题。Input应该batch_shape按照目前的规定。下次请确保您的代码有效，会更容易。

解决方案

解决方案很简单；您传递给网络的批次尺寸错误。

677376 / 21168 = 32它是predict预期的批次的默认大小。如果它不同（在您的情况下为 50），您应该指定它，如下所示：

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，如果你想硬编码，记得指定批量大小。

于 2019-03-08T00:14:37.853 回答