How to get the latent vector as an output from a cnn model before training to the fully connected layer?

Question

I am working on CNN model using Tensorflow frames in google collab. I am unable to extract the latent vectors from the convolutional layers. I want to extract the output of the convolutional layers, the layers before fully connected layer. I have tried with the following code

a = dropout()(classifier_model.output)
print(a)

I am unable to understand the solution suggested on the link Stackoverflow solution to print the value of tensorflow object after applying a-conv-pool-layer

Anyone with any suggestion?

Answer 1

You can use get_layer method of the Model class to get a layer by its name, find bellow an example with a dummy 1D CNN and a binary classifier :

timesteps = 100
nfeatures = 2

# build the model using the functional API
# example of a 1D CNN inspired by the your stack overflow link, but using a model instead of successive *raw* layers
# the values of the Conv1D filters and kernels are different
input = Input((timesteps, nfeatures))

p = Conv1D(filters=16, kernel_size=10)(input)
p = ReLU()(p)
p = MaxPool1D(pool_size=2)(p)
p = Conv1D(filters=32, kernel_size=10)(p)
p = ReLU()(p)
p = MaxPool1D(pool_size=2)(p)
p = Conv1D(filters=64, kernel_size=10)(p)
p = ReLU()(p)
p = MaxPool1D(pool_size=2, name='conv1Dfeat')(p)  # give a name to the CNN output
# fully connected part
p = Flatten()(p)
p = Dense(10)(p)
# could add a dropout layer to ease optimization
finaloutput = Dense(1, activation='sigmoid')(p)

# full model
model = Model(inputs=input, outputs=finaloutput)

# compile network, i.e. define optimizer, loss and metrics
model.compile(optimizer='Adam', loss='binary_crossentropy', metrics=['accuracy'])
model.summary()

You need to train the model using the fit method with some data. Then you can get the output of the layer which name is conv1Dfeat (the last layer of the convolutive part) by defining the model:

modelCNN = Model(inputs=input, outputs=model.get_layer('conv1Dfeat').output)
modelCNN.summary()

If you want to get the output of the convolutive part, let's say based on a single numpy input array of shape (timesteps, nfeatures), you can use the predict of the Model class on batched data:

data = np.random.normal(size=(timesteps, nfeatures))  # dummy data
data_tf = tf.expand_dims(data, axis=0)  # convert to TF tensor and add batch dimension at the same time
cnn_out_np = modelCNN.predict(data_tf)
cnn_out_np = np.squeeze(cnn_out_np, axis=0)  # remove batch dimension
print(cnn_out_np.shape)

(4, 64)