Putting the model together, and incorporating our new cool multi-GPU feature, we come up with the following architecture:
def build_network(num_gpu=1, input_shape=None): inputs = Input(shape=input_shape, name="input") # convolutional block 1 conv1 = Conv2D(64, kernel_size=(3,3), activation="relu", name="conv_1")(inputs) batch1 = BatchNormalization(name="batch_norm_1")(conv1) pool1 = MaxPooling2D(pool_size=(2, 2), name="pool_1")(batch1) # convolutional block 2 conv2 = Conv2D(32, kernel_size=(3,3), activation="relu", name="conv_2")(pool1) batch2 = BatchNormalization(name="batch_norm_2")(conv2) pool2 = MaxPooling2D(pool_size=(2, 2), name="pool_2")(batch2) # fully connected layers flatten = Flatten()(pool2) fc1 = Dense(512, activation ...
