red

gistfile1.txt

net_input = Input(shape=(128, 87, 1))

def inception_block(input_layer,filters=64):
    tower_1 = Conv2D(filters, (1,1), padding='same', activation='relu')(input_layer)
    tower_1 = Conv2D(filters, (3,3), padding='same', activation='relu')(tower_1)
    tower_2 = Conv2D(filters, (1,1), padding='same', activation='relu')(input_layer)
    tower_2 = Conv2D(64, (5,5), padding='same', activation='relu')(tower_2)
    tower_3 = MaxPooling2D((3,3), strides=(1,1), padding='same')(input_layer)
    tower_3 = Conv2D(filters, (1,1), padding='same', activation='relu')(tower_3)
    output = concatenate([tower_1, tower_2, tower_3], axis = 3)
    return output


net=inception_block(net_input,filters=16)
net=inception_block(net,filters=16)

net=MaxPooling2D((5,5), strides=3)(net)

net=Flatten()(net)

net=Dense(units=256, activation='relu')(net)
net=Dropout(0.1)(net)
net=Dense(units=9, activation = 'softmax')(net)

model=Model(net_input,net)

model.compile(loss='categorical_crossentropy', optimizer='Adam', metrics=['accuracy'])
model.summary()