[英]Manipulating output of neural network
我有一个神经网络,需要输入(m,2,3,96,96)和输出(m,2,128)。 我试图通过减去output [m] [0] [0]-output [m] [0] [1]将输出转换为(m,1,128),然后通过馈入将输出转换为(m,1) 1x128输出到密集层
我已经尝试过Lambda和keras.backend。减去网络和包装器中的层
def faceRecoModel(input_shape):
"""
Implementation of the Inception model used for FaceNet
Arguments:
input_shape -- shape of the images of the dataset
Returns:
model -- a Model() instance in Keras
"""
# Define the input as a tensor with shape input_shape
X_input = Input(input_shape)
# Zero-Padding
X = ZeroPadding2D((3, 3))(X_input)
# First Block
X = Conv2D(64, (7, 7), strides=(2, 2), name='conv1')(X)
X = BatchNormalization(axis=1, name='bn1')(X)
X = Activation('relu')(X)
# Zero-Padding + MAXPOOL
X = ZeroPadding2D((1, 1))(X)
X = MaxPooling2D((3, 3), strides=2)(X)
# Second Block
X = Conv2D(64, (1, 1), strides=(1, 1), name='conv2')(X)
X = BatchNormalization(axis=1, epsilon=0.00001, name='bn2')(X)
X = Activation('relu')(X)
# Zero-Padding + MAXPOOL
X = ZeroPadding2D((1, 1))(X)
# Second Block
X = Conv2D(192, (3, 3), strides=(1, 1), name='conv3')(X)
X = BatchNormalization(axis=1, epsilon=0.00001, name='bn3')(X)
X = Activation('relu')(X)
# Zero-Padding + MAXPOOL
X = ZeroPadding2D((1, 1))(X)
X = MaxPooling2D(pool_size=3, strides=2)(X)
# Inception 1: a/b/c
X = inception_block_1a(X)
X = inception_block_1b(X)
X = inception_block_1c(X)
# Inception 2: a/b
X = inception_block_2a(X)
X = inception_block_2b(X)
# Inception 3: a/b
X = inception_block_3a(X)
X = inception_block_3b(X)
# Top layer
X = AveragePooling2D(pool_size=(3, 3), strides=(1, 1), data_format='channels_first')(X)
X = Flatten()(X)
X = Dense(128, name='dense_layer')(X)
# L2 normalization
X = Lambda(lambda x: K.l2_normalize(x, axis=1))(X)
# Create model instance
model = Model(inputs=X_input, outputs=X, name='FaceRecoModel')
return model
# now this is the wrapper I mentioned
model = faceRecoModel((3, 96, 96))
i = Input((2, 3, 96, 96))
o = TimeDistributed(model)(i)
model = Model(i, o)
model.compile(optimizer='adam', loss=pair_loss)
X = Lambda(lambda x: return x[:,0] - x[:,1])(X)
X = Dense(...)(X)
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