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.