Viewing intermediate activations: too many indices for array: array is 2-dimensional, but 4 were indexed

Question

I wrote this model:

def create_network(): 
  model = Sequential()
  model.add(Input(shape=(150,150,3)))

  model.add(Conv2D(32, kernel_size=3,strides=(1, 1),activation='relu',kernel_initializer="glorot_uniform", padding='valid', dilation_rate=1))
  model.add(MaxPooling2D(pool_size=(2, 2)))
  model.add(Dropout(0.5))

  model.add(Conv2D(64, kernel_size=3, strides=(1, 1), activation='relu',kernel_initializer="glorot_uniform", padding='valid', dilation_rate=1))
  model.add(MaxPooling2D(pool_size=(2, 2)))
  model.add(Dropout(0.5))

  model.add(Conv2D(128, kernel_size=3, strides=(1, 1), activation='relu',kernel_initializer="glorot_uniform",  padding='valid', dilation_rate=1))
  model.add(MaxPooling2D(pool_size=(2, 2)))
  model.add(Dropout(0.5))

  model.add(Conv2D(128, kernel_size=3, strides=(1, 1), activation='relu',kernel_initializer="glorot_uniform",padding='valid', dilation_rate=1))
  model.add(MaxPooling2D(pool_size=(2, 2)))
  model.add(Dropout(0.5))

  model.add(Flatten())
  model.add(Dense(512, activation='relu'))
  model.add(Dropout(0.5))

  model.add(Dense(1, activation='sigmoid'))
  model.compile(optimizer = 'adam',
                   loss = 'binary_crossentropy', 
                   metrics = ['accuracy'])
  return model

create_network()

And I fit it to some data:

def fit_model(train_generator=train_generator, validation_generator=validation_generator,network=create_network()):
  checkpoint_path = "/content/drive/model_checkpoint.h5"
  checkpoint_dir = os.path.dirname(checkpoint_path)

  callbacks_list = [
      callbacks.EarlyStopping(
          monitor = 'accuracy',
          patience = 2,
      ),

      callbacks.ModelCheckpoint(
          filepath=checkpoint_path,
          monitor = 'val_loss',
          #save_weights_only=True,
          save_best_only=True,
      ),
  ]



  model = network
  history = model.fit(train_generator,
                      epochs=1000,
                      validation_data=validation_generator,
                      callbacks = callbacks_list,
                      verbose=1
                      )
  score = model.evaluate(validation_generator)
  return history,model

history,model = fit_model(train_generator,validation_generator)

I can read in the model:

model = load_model('/content/drive/model_checkpoint.h5')

For an image, I want to visualise every channel in every intermediate activation for the model.

I can read in the image:

from keras.preprocessing import image
file_list = ['/content/drive/image.JPEG']
file_list = file_list[0]


test_image = image.load_img(file_list,target_size=(150,150))
images = image.img_to_array(test_image)
images /= 255.0
images = np.expand_dims(images, axis=0)

To visualise the activations, I wrote:

layer_outputs = [layer.output for layer in model.layers[:]]
activation_model = tf.keras.Model(inputs=model.input, outputs=layer_outputs)
activations = activation_model.predict(images)


for i in range(len(layer_outputs)):
  first_layer_activation = activations[i]
  print(first_layer_activation.shape)
  plt.matshow(first_layer_activation[0, :, :, 1], cmap='viridis')

The output prints:

(1, 148, 148, 32)
(1, 74, 74, 32)
(1, 74, 74, 32)
(1, 72, 72, 64)
(1, 36, 36, 64)
(1, 36, 36, 64)
(1, 34, 34, 128)
(1, 17, 17, 128)
(1, 17, 17, 128)
(1, 15, 15, 128)
(1, 7, 7, 128)
(1, 7, 7, 128)
(1, 6272)

And then an error that says:

      2   first_layer_activation = activations[i]
      3   print(first_layer_activation.shape)
----> 4   plt.matshow(first_layer_activation[0, :, :, 1], cmap='viridis')

IndexError: too many indices for array: array is 2-dimensional, but 4 were indexed

Followed by some images.

Could someone explain what that error is saying and/or how to fix it? I'm not understanding how I get an error, but then it prints some intermediate activations images, so then I'm not sure I've done this properly?