def create_model(noise=True,
first_kernel_size=(7, 7),
n_filters=64,
n_covul_layers=5,
activation='swish',
dense_neurons=1024,
dropout=0.5,
lr=0.0001):
kernel = (3, 3)
n_classes = len(classes)
input_layer = Input(shape=(300, 300, 3))
if noise:
input_layer = GaussianNoise(0.1)(input_layer)
model = BatchNormalization(axis=[1, 2])(input_layer)
model = Conv2D(filters=n_filters,
kernel_size=first_kernel_size,
activation=activation)(model)
model = BatchNormalization(axis=[1, 2])(model)
model = MaxPooling2D((2, 2))(model)
for i in range(2, n_covul_layers):
model = Conv2D(filters=n_filters * i,
kernel_size=kernel,
activation=activation)(model)
model = Conv2D(filters=n_filters * i,
kernel_size=kernel,
activation=activation,
padding='same')(model)
model = BatchNormalization(axis=[1, 2])(model)
model = MaxPooling2D((2, 2))(model)
model = Conv2D(filters=n_filters * (n_covul_layers + 1),
kernel_size=kernel,
activation=activation,
padding='same')(model)
model = Conv2D(filters=n_filters * (n_covul_layers + 1),
kernel_size=kernel,
activation=activation,
padding='same')(model)
model = BatchNormalization(axis=[1, 2])(model)
model = MaxPooling2D((2, 2))(model)
model = Flatten()(model)
model = Dense(dense_neurons, activation=activation)(model)
model = BatchNormalization()(model)
model = Dropout(dropout)(model)
model = Dense(dense_neurons / 2, activation=activation)(model)
model = BatchNormalization()(model)
model = Dropout(dropout)(model)
output = Dense(n_classes, activation="softmax")(model)
model = Model(input_layer, output)
model.compile(loss="sparse_categorical_crossentropy",
optimizer=keras.optimizers.Adam(lr=lr),
metrics=["accuracy"])
return model
