find number of convolutional and dense layers

Question

I copy code from Kaggle and I can not count the numbers of layers in it. I am working on an image classification model. can anyone explain this. I try most solutions and I can not count the convolutional and dense layers.

model = Sequential()
inputShape = (height, width, depth)
chanDim = -1
if K.image_data_format() == "channels_first":
    inputShape = (depth, height, width)
    chanDim = 1
    

model.add(Conv2D(32, (3, 3), padding="same",input_shape=inputShape))
model.add(Activation("relu"))
model.add(BatchNormalization(axis=chanDim))
model.add(MaxPooling2D(pool_size=(3, 3)))
model.add(Dropout(0.25))

model.add(Conv2D(64, (3, 3), padding="same"))
model.add(Activation("relu"))
model.add(BatchNormalization(axis=chanDim))

model.add(Conv2D(64, (3, 3), padding="same"))
model.add(Activation("relu"))
model.add(BatchNormalization(axis=chanDim))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))

model.add(Conv2D(128, (3, 3), padding="same"))
model.add(Activation("relu"))
model.add(BatchNormalization(axis=chanDim))

model.add(Conv2D(128, (3, 3), padding="same"))
model.add(Activation("relu"))
model.add(BatchNormalization(axis=chanDim))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Flatten())

model.add(Dense(1024))
model.add(Activation("relu"))
model.add(BatchNormalization())
model.add(Dropout(0.5))

model.add(Dense(15))
model.add(Activation("softmax"))

model.summary()

can any one explain.

Model: "sequential_2"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
conv2d_5 (Conv2D)            (None, 256, 256, 32)      896       
_________________________________________________________________
activation_6 (Activation)    (None, 256, 256, 32)      0         
_________________________________________________________________
batch_normalization_6 (Batch (None, 256, 256, 32)      128       
_________________________________________________________________
max_pooling2d_3 (MaxPooling2 (None, 85, 85, 32)        0         
_________________________________________________________________
dropout_4 (Dropout)          (None, 85, 85, 32)        0         
_________________________________________________________________
conv2d_6 (Conv2D)            (None, 85, 85, 64)        18496     
_________________________________________________________________
activation_7 (Activation)    (None, 85, 85, 64)        0         
_________________________________________________________________
batch_normalization_7 (Batch (None, 85, 85, 64)        256       
_________________________________________________________________
conv2d_7 (Conv2D)            (None, 85, 85, 64)        36928     
_________________________________________________________________
activation_8 (Activation)    (None, 85, 85, 64)        0         
_________________________________________________________________
batch_normalization_8 (Batch (None, 85, 85, 64)        256       
_________________________________________________________________
max_pooling2d_4 (MaxPooling2 (None, 42, 42, 64)        0         
_________________________________________________________________
dropout_5 (Dropout)          (None, 42, 42, 64)        0         
_________________________________________________________________
conv2d_8 (Conv2D)            (None, 42, 42, 128)       73856     
_________________________________________________________________
activation_9 (Activation)    (None, 42, 42, 128)       0         
_________________________________________________________________
batch_normalization_9 (Batch (None, 42, 42, 128)       512       
_________________________________________________________________
conv2d_9 (Conv2D)            (None, 42, 42, 128)       147584    
_________________________________________________________________
activation_10 (Activation)   (None, 42, 42, 128)       0         
_________________________________________________________________
batch_normalization_10 (Batc (None, 42, 42, 128)       512       
_________________________________________________________________
max_pooling2d_5 (MaxPooling2 (None, 21, 21, 128)       0         
_________________________________________________________________
dropout_6 (Dropout)          (None, 21, 21, 128)       0         
_________________________________________________________________
flatten_1 (Flatten)          (None, 56448)             0         
_________________________________________________________________
dense_1 (Dense)              (None, 1024)              57803776  
_________________________________________________________________
activation_11 (Activation)   (None, 1024)              0         
_________________________________________________________________
batch_normalization_11 (Batc (None, 1024)              4096      
_________________________________________________________________
dropout_7 (Dropout)          (None, 1024)              0         
_________________________________________________________________
dense_2 (Dense)              (None, 15)                15375     
_________________________________________________________________
activation_12 (Activation)   (None, 15)                0         
=================================================================
Total params: 58,102,671
Trainable params: 58,099,791
Non-trainable params: 2,880
_________________________________________________________________
 

I can not count the numbers of convolutional and dense layers. I try model.layers as well. the output of this is 28. how?

How can I get the number of convolutional & dense layers programmatically?

Answer 1

First, the reason why the number of layers is 28 is because Flatten, BatchNormalization, Dropout, Activation and MaxPool2D are all counted in model.layers.

That being said, you can get the count of the layers using isinstance:

num_conv = 0
num_dense = 0
for layer in model.layers:
    if isinstance(layer, Conv2D):
        num_conv += 1
    elif isinstance(layer, Dense):
        num_dense += 1