I have an image dataset 376 classes each class has 15 pictures corresponds to a person. I would like to get the feature vector that corresponds to each person.

What I have done is, after I compiled the model I then used this link
as a reference to get the weights of the last convolutional layer. However, when I do this, I get the error:

InvalidArgumentError: You must feed a value for placeholder tensor 'conv_layer' with dtype float and shape [?,19,19,360]

How can I resolve this issue?

Here is the code that I have done so far:

train_data = np.array(train_data, dtype=np.float32)

test_data = np.array(test_data, dtype=np.float32)

train_data = train_data / 180 # to make the array values between 0-1 

test_data = test_data / 180

train_label = keras.utils.to_categorical(train_label, 376)

test_label = keras.utils.to_categorical(test_label, 376)

# CNN MODEL

model = Sequential()

model.add(Conv2D(180, (3, 3), padding='same', input_shape=(180, 180, 3), 

activation="relu")) #180 is the number of filters

model.add(Conv2D(180, (3, 3), activation="relu"))

model.add(MaxPooling2D(pool_size=(3, 3))) 

model.add(Dropout(0.25))

model.add(Conv2D(360, (3, 3), padding='same', activation="relu"))

model.add(Conv2D(360, (3, 3), activation="relu"))

conv_layer = model.add(MaxPooling2D(pool_size=(3, 3)))

model.add(Dropout(0.25))

flatten_layer = model.add(Flatten())

model.add(Dense(496, activation="relu"))

model.add(Dropout(0.5))

dense_layer = model.add(Dense(376, activation="softmax"))

#compiling the model

model.compile(

loss='categorical_crossentropy',

optimizer='adam',

metrics=['accuracy']

)

model.fit(

train_data,

train_label,

batch_size=32,

epochs=40,

verbose = 2 ,

validation_split=0.1,

shuffle=True)

# getting intermediate  layer weights 

get_layer_output = K.function([model.layers[0].input],

                              [model.layers[11].output])

layer_output = get_layer_output([conv_layer])[0]

The easiest way to create a truncated output from a network is create a sub-network of it and apply weights of your trained network. The following example is a modification of what you have shown up there, but it will guide you out

Network you want to train originally

model = Sequential()

model.add(Conv2D(10, (3, 3), padding='same', input_shape=(60, 60, 3),

                 activation="relu"))

model.add(Conv2D(10, (3, 3), activation="relu"))

model.add(MaxPooling2D(pool_size=(3, 3)))

model.add(Dropout(0.25))

model.add(Flatten())

model.add(Dense(5, activation="softmax"))

model.compile(

    loss='categorical_crossentropy',

    optimizer='adam',

    metrics=['accuracy'])



model.fit(

    train_data,

    train_label)

Now create a subnetwork from which you want the outputs, like from above example

model_new = Sequential()

model_new.add(Conv2D(10, (3, 3), padding='same', input_shape=(60, 60, 3),

                     activation="relu"))

model_new.add(Conv2D(10, (3, 3), activation="relu"))

model_new.add(MaxPooling2D(pool_size=(3, 3)))

model_new.add(Dropout(0.25))

model_new.add(Flatten())



model_new.compile(

    loss='categorical_crossentropy',

    optimizer='adam',

    metrics=['mse'])



# You need to apply fit on random array's created, just so as to initialise 

# weights. Anyways you will replacing them with original ones from above.

model_new.fit(train_data, y=np.random.rand(40, 3610))

Now take weights from the first trained network and replace the weights of the second network like

model_new.set_weights(weights=model.get_weights())

You can check whether the weights are changed in the above step by actually adding these check statements like

print("Are arrays equal before fit - ",

      any([np.array_equal(a1, a2) for a1, a2 in zip(model_new.get_weights(), model.get_weights()[:4])]))



model_new.set_weights(weights=model.get_weights())

print("Are arrays equal after applying weights - ",

      all([np.array_equal(a1, a2) for a1, a2 in zip(model_new.get_weights(), model.get_weights()[:4])]))

This should yeild

Are arrays equal before fit -  False

Are arrays equal after applying weights -  True

Hope this helps.