The input shape in the first Conv2D layer is supposed to be (100, 100, 1) however the output is (None, 98, 98, 200). I understand what 200 and None determine but I'm not sure about 98 as the parameter. Also, adding to this, I randomly selected 200 as the number of filters in Conv2D for my model. How should I determine a suitable number of filters for my model. Is it based on trial and error? Please help. Thanks!!
from keras.models import Sequential
from keras.layers import Dense, Activation, Flatten, Dropout
from keras.layers import Conv2D, MaxPooling2D
from keras.callbacks import ModelCheckpoint
print(data.shape[1:])
model = Sequential()
model.add(Conv2D(200, (3,3), input_shape = data.shape[1:]))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size = (2,2)))
model.add(Conv2D(100,(3,3)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2,2)))
model.add(Flatten())
model.add(Dropout(0.5))
model.add(Dense(50, activation = 'relu'))
model.add(Dense(2, activation = 'softmax'))
model.compile(loss = 'categorical_crossentropy', optimizer = 'adam', metrics = ['accuracy'])
model.summary()
(100, 100, 1) Model: "sequential_3"
Layer (type) Output Shape Param #
conv2d_5 (Conv2D) (None, 98, 98, 200) 2000
activation_5 (Activation) (None, 98, 98, 200) 0
max_pooling2d_5 (MaxPooling2 (None, 49, 49, 200) 0
conv2d_6 (Conv2D) (None, 47, 47, 100) 180100
activation_6 (Activation) (None, 47, 47, 100) 0
max_pooling2d_6 (MaxPooling2 (None, 23, 23, 100) 0
flatten_3 (Flatten) (None, 52900) 0
dropout_3 (Dropout) (None, 52900) 0
dense_5 (Dense) (None, 50) 2645050
dense_6 (Dense) (None, 2) 102
Total params: 2,827,252 Trainable params: 2,827,252 Non-trainable params: 0
