how to extract output of a layer of model to numpy array-CodePudding

def build(width, height, depth, classes, weightsPath=None):
        # initialize the model
        model = Sequential()
        model.add(Conv2D(100, (5, 5), padding="same",input_shape=(depth, height, width), data_format="channels_first"))
        model.add(Activation("relu"))
        model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2),data_format="channels_first"))

        model.add(Conv2D(100, (5, 5), padding="same"))
        model.add(Activation("relu"))
        model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2), data_format="channels_first"))

        # 3 set of CONV => RELU => POOL
        model.add(Conv2D(100, (5, 5), padding="same"))
        model.add(Activation("relu"))
        model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2),data_format="channels_first"))

        # 4 set of CONV => RELU => POOL
        model.add(Conv2D(50, (5, 5), padding="same"))
        model.add(Activation("relu"))
        model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2),data_format="channels_first"))

        # 5 set of CONV => RELU => POOL
        model.add(Conv2D(50, (5, 5), padding="same"))
        model.add(Activation("relu"))
        model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2), data_format="channels_first"))

        # 6 set of CONV => RELU => POOL
        model.add(Conv2D(50, (5, 5), padding="same"))
        model.add(Activation("relu"))
        model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2), data_format="channels_first"))

        # set of FC => RELU layers
        model.add(Flatten())
        #model.add(Dense(classes))
        #model.add(Activation("relu"))

        # softmax classifier
        model.add(Dense(classes))
        model.add(Activation("softmax"))

        return model
test_model = build(width=200, height=200, depth=1, classes=100) 
epochs=50
batch_size=128
cnn_model.compile(optimizer='Adam', loss='mse')
history = test_model.fit(X_train, y_train,validation_data=[X_valid,y_valid],epochs=epochs,batch_size=batch_size,
                   verbose=1)

I want to extract the output of the intermediate layer which is provided below as numpy array and want to save it to a text file

The output of the layer I want to extract is

# 6 set of CONV => RELU => POOL

model.add(Conv2D(50, (5, 5), padding="same"))

I tried links from here Keras, How to get the output of each layer?

However i am unable to incorporate the solution provided in link to my problem. I hope experts may help me overcoming this problem.

CodePudding user response：

You can do it in this way:

from tensorflow.keras.models import Model
cnn_model = build(...) # build your model by invoking your function
layer_idx = 6
# Indices are based on order of horizontal graph traversal (bottom-up).
layer_to_interpret = cnn_model.get_layer(index=layer_idx)
# You can also use the name of layer to get it.
# layer_to_interpret = cnn_model.get_layer(layer_name)
# Create multi-output model
multiout_model = Model(inputs=cnn_model.inputs, outputs=[layer_to_interpret.output, cnn_model.output])

conv_outs, predictions = multiout_model(images)
# save conv_outs to a file

CodePudding user response：

Note that the Sequential constructor accepts a name argument, so to make things easy and unambiguous, add name feature to the layer you want to extract its output :

# 6 set of CONV => RELU => POOL

model.add(Conv2D(50, (5, 5), padding="same", name="my_intermediate_layer"))

# to extract output :
outputs=model.get_layer(name="my_intermediate_layer").output