[英]How to get the output shape of a layer in Keras?
I have the following code in Keras (Basically I am modifying this code for my use) and I get this error:我在 Keras 中有以下代码(基本上我正在修改此代码以供我使用)并且我收到此错误:
'ValueError: Error when checking target: expected conv3d_3 to have 5 dimensions, but got array with shape (10, 4096)' 'ValueError:检查目标时出错:预期 conv3d_3 有 5 个维度,但得到的数组形状为 (10, 4096)'
Code:代码:
from keras.models import Sequential
from keras.layers.convolutional import Conv3D
from keras.layers.convolutional_recurrent import ConvLSTM2D
from keras.layers.normalization import BatchNormalization
import numpy as np
import pylab as plt
from keras import layers
# We create a layer which take as input movies of shape
# (n_frames, width, height, channels) and returns a movie
# of identical shape.
model = Sequential()
model.add(ConvLSTM2D(filters=40, kernel_size=(3, 3),
input_shape=(None, 64, 64, 1),
padding='same', return_sequences=True))
model.add(BatchNormalization())
model.add(ConvLSTM2D(filters=40, kernel_size=(3, 3),
padding='same', return_sequences=True))
model.add(BatchNormalization())
model.add(ConvLSTM2D(filters=40, kernel_size=(3, 3),
padding='same', return_sequences=True))
model.add(BatchNormalization())
model.add(ConvLSTM2D(filters=40, kernel_size=(3, 3),
padding='same', return_sequences=True))
model.add(BatchNormalization())
model.add(Conv3D(filters=1, kernel_size=(3, 3, 3),
activation='sigmoid',
padding='same', data_format='channels_last'))
model.compile(loss='binary_crossentropy', optimizer='adadelta')
the data I feed is in the following format: [1, 10, 64, 64, 1].我提供的数据格式如下:[1, 10, 64, 64, 1]。 So I would like to know where I am wrong and also how to see the output_shape of each layer.
所以我想知道我错在哪里以及如何查看每一层的 output_shape。
You can get the output shape of a layer by layer.output_shape
.您可以通过
layer.output_shape
获得层的输出形状。
for layer in model.layers:
print(layer.output_shape)
Gives you:给你:
(None, None, 64, 64, 40)
(None, None, 64, 64, 40)
(None, None, 64, 64, 40)
(None, None, 64, 64, 40)
(None, None, 64, 64, 40)
(None, None, 64, 64, 40)
(None, None, 64, 64, 40)
(None, None, 64, 64, 40)
(None, None, 64, 64, 1)
Alternatively you can pretty print the model using model.summary
:或者,您可以使用
model.summary
漂亮地打印模型:
model.summary()
Gives you the details about the number of parameters and output shapes of each layer and an overall model structure in a pretty format:以漂亮的格式为您提供有关每层的参数数量和输出形状以及整体模型结构的详细信息:
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
conv_lst_m2d_1 (ConvLSTM2D) (None, None, 64, 64, 40) 59200
_________________________________________________________________
batch_normalization_1 (Batch (None, None, 64, 64, 40) 160
_________________________________________________________________
conv_lst_m2d_2 (ConvLSTM2D) (None, None, 64, 64, 40) 115360
_________________________________________________________________
batch_normalization_2 (Batch (None, None, 64, 64, 40) 160
_________________________________________________________________
conv_lst_m2d_3 (ConvLSTM2D) (None, None, 64, 64, 40) 115360
_________________________________________________________________
batch_normalization_3 (Batch (None, None, 64, 64, 40) 160
_________________________________________________________________
conv_lst_m2d_4 (ConvLSTM2D) (None, None, 64, 64, 40) 115360
_________________________________________________________________
batch_normalization_4 (Batch (None, None, 64, 64, 40) 160
_________________________________________________________________
conv3d_1 (Conv3D) (None, None, 64, 64, 1) 1081
=================================================================
Total params: 407,001
Trainable params: 406,681
Non-trainable params: 320
_________________________________________________________________
If you want to access information about a specific layer only, you can use name
argument when constructing that layer and then call like this:如果您只想访问有关特定层的信息,则可以在构造该层时使用
name
参数,然后像这样调用:
...
model.add(ConvLSTM2D(..., name='conv3d_0'))
...
model.get_layer('conv3d_0')
EDIT: For reference sake it will always be same as layer.output_shape
and please don't actually use Lambda or custom layers for this.编辑:为了参考起见,它总是与
layer.output_shape
相同,请不要为此实际使用 Lambda 或自定义层。 But you can use Lambda
layer to echo the shape of a passing tensor.但是您可以使用
Lambda
层来回显传递张量的形状。
...
def print_tensor_shape(x):
print(x.shape)
return x
model.add(Lambda(print_tensor_shape))
...
Or write a custom layer and print the shape of the tensor on call()
.或者编写一个自定义层并在
call()
上打印张量的形状。
class echo_layer(Layer):
...
def call(self, x):
print(x.shape)
return x
...
model.add(echo_layer())
You can get the output shape with 'output.shape[1:]' command.您可以使用 'output.shape[1:]' 命令获取输出形状。 It will get the shape of output layer and can be used for other purposes.
它将获得输出层的形状,并可用于其他目的。
声明:本站的技术帖子网页,遵循CC BY-SA 4.0协议,如果您需要转载,请注明本站网址或者原文地址。任何问题请咨询:yoyou2525@163.com.