Keras (Tensorflow) Reshape Layer 输入错误
Keras (Tensorflow) Reshape Layer input error
我有一个重塑输入错误，我不知道为什么。
请求的形状是 1058400，即 (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之前的层是一个flatten层，我在定义Reshape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)
总结：
__________________________________________________________________________________________________
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：
以上代码错误：
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/Whosebug/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.
问题版本
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 应采用当前提供的 batch_shape。下次请确保您的代码有效，会更容易。
解决方案
解决方法很简单；您传递到网络的批次尺寸错误。
677376 / 21168 = 32 它是 批处理的默认大小 ，这是 predict 所期望的。如果它不同（在你的例子中是 50），你应该指定它，像这样：
out = model.predict(np.random.randint(1, 5, size=(50, 230, 230, 1)), batch_size=50)
现在一切都应该工作正常，如果你想硬编码，请记住指定批处理大小。
我有一个重塑输入错误，我不知道为什么。 请求的形状是 1058400，即 (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之前的层是一个flatten层，我在定义Reshape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)
总结：
__________________________________________________________________________________________________
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：
以上代码错误：
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/Whosebug/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.
问题版本
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 应采用当前提供的 batch_shape。下次请确保您的代码有效，会更容易。
解决方案
解决方法很简单；您传递到网络的批次尺寸错误。
677376 / 21168 = 32 它是 批处理的默认大小 ，这是 predict 所期望的。如果它不同（在你的例子中是 50），你应该指定它，像这样：
out = model.predict(np.random.randint(1, 5, size=(50, 230, 230, 1)), batch_size=50)
现在一切都应该工作正常，如果你想硬编码，请记住指定批处理大小。