Question

我有一个重塑输入错误，我不知道为什么。要求的形状为1058400，即（1，21168）乘以50的批量大小。我不明白的是677376的表观输入大小。我不知道这个值从哪里来。重塑之前的层是一个平坦的层，当我定义重塑层的目标形状时，我直接使用它的形状。

模型编译得很好，我使用Tensorflow作为后端，因此它在运行时定义。但是该错误仅在我输入日期时出现。

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)

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.

Answer 1

问题的版本

重要提示：我已经编辑了您的问题，因此它可以实际运行并代表您的问题。 Input应该采用batch_shape当前提供的格式。下次，请确保您的代码可以正常使用，

解决方案

解决方案非常简单；您传递到网络的批次的尺寸错误。

677376 / 21168 = 32是 predict所期望的默认批次大小。您应该指定它是否不同（在您的情况下为50），如下所示：

out = model.predict(np.random.randint(1, 5, size=(50, 230, 230, 1)), batch_size=50)

现在一切都可以正常工作，如果要对其进行硬编码，请记住指定批次大小。

Keras（Tensorflow）重塑图层输入错误

1 个答案:

问题的版本

解决方案