keras中獲取層輸出shape的方法彙總

【時間】2018.12.24

【題目】keras中獲取層輸出shape的方法彙總

在keras 中，要想獲取層輸出shape，可以先獲取層物件，再通過層物件的屬性output或者output_shape獲得層輸出shape(若要獲取層輸入shape,可以用input/input_shape)。兩者的輸出分別為：

output:

output_shape:

獲取層物件的方法有兩種，一種是使用model.get_layer()方法，另一種是使用model.layers[index]。

當然，你也可以使用model.summary（）列印出整個模型，從而可以獲取層輸出shape。

使用model.get_layer(self，name=none，index=none)：依據層名或下標獲得層物件model.get_layer(self，name=none，index=none)

具體為：

1.1 特定層輸出：

model.get_layer(index=0).output或者

model.get_layer(index=0).output_shape

for i in range(len(model.layers)):

print(model.get_layer(index=i).output)

2.1 特定層輸出：

model.layers[0].output或者

model.layers[0].output_shape

for layer in model.layers:

print(layer.output)

from keras.models import model

from keras.layers import dense, dropout, activation, flatten,input
from keras.layers import conv2d, maxpooling2d
x = input(shape=(96,96,3))
conv1_1 = conv2d(64,kernel_size=(3,3),padding='valid', activation='relu', name='conv_1')(x)
conv1_2 = conv2d(64,kernel_size=(3,3),padding='same', activation='relu', name='conv_2')(conv1_1)
pool1_1 = maxpooling2d((2, 2), strides=(2, 2), name='pool_frame_1')(conv1_2)
conv1_3 = conv2d(128,kernel_size=(3,3),padding='same', activation='relu', name='conv_3')(pool1_1)
conv1_4 = conv2d(128,kernel_size=(3,3),padding='same', activation='relu', name='conv_4')(conv1_3)
pool1_2 = maxpooling2d((2,2), strides=(2, 2), name='pool_frame_2')(conv1_4)
conv_5 = conv2d(256,kernel_size=(3,3),padding='same', activation='relu', name='conv_5')(pool1_2)
conv_6 = conv2d(256,kernel_size=(3,3),padding='same', activation='relu', name='conv_6')(conv_5)
conv_7 = conv2d(256,kernel_size=(3,3),padding='same', activation='relu', name='conv_7')(conv_6)
pool_3 = maxpooling2d((2,2), strides=(2, 2), name='pool_final_3')(conv_7)
conv_8 = conv2d(512,kernel_size=(3,3),padding='same', activation='relu', name='conv_8')(pool_3)
conv_9 = conv2d(512,kernel_size=(3,3),padding='same', activation='relu', name='conv_9')(conv_8)
conv_10 = conv2d(512,kernel_size=(3,3),padding='same', activation='relu', name='conv_10')(conv_9)
pool_4 = maxpooling2d((2,2), strides=(2, 2), name='pool_final_4')(conv_10)
conv_11 = conv2d(512,kernel_size=(3,3),padding='same', activation='relu', name='conv_11')(pool_4)
conv_12 = conv2d(512,kernel_size=(3,3),padding='same', activation='relu', name='conv_12')(conv_11)
conv_13 = conv2d(512,kernel_size=(3,3),padding='same', activation='relu', name='conv_13')(conv_12)
pool_5 = maxpooling2d((2,2), strides=(2, 2), name='pool_final_5')(conv_13)
flatten = flatten()(pool_5)
fc1= dense(256, activation='relu')(flatten)
out_put = dense(2, activation='softmax')(fc1)
model = model(inputs=x, outputs=out_put)
model.compile(loss='categorical_crossentropy',
optimizer='adadelta',
metrics=['accuracy'])
print('method 3:')
model.summary()  # method 3
print('method 1:')
for i in range(len(model.layers)):
print(model.get_layer(index=i).output)
print('method 2:')
for layer in model.layers:
print(layer.output_shape)

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