def build_model_three_modules(num_filters):
input_shape_img = deep_utils.get_rgb_input_shape(IMG_ROWS, IMG_COLUMNS)
#num_filters = 32
inputs = Input(shape=input_shape_img)
x = Conv2D(num_filters, kernel_size=(3, 3), activation='relu')(inputs)
x_prime = resnet_module_full_preactivation(x, num_filters)
x = keras.layers.add([x, x_prime])
x_prime = resnet_module_full_preactivation(x, num_filters)
x = keras.layers.add([x, x_prime])
x_prime = resnet_module_full_preactivation(x, num_filters)
x = keras.layers.add([x, x_prime])
y = GlobalAveragePooling2D()(x)
#y = Flatten()(x)
outputs = Dense(NUM_CLASSES, activation='softmax')(y)
model = Model(inputs=inputs, outputs=outputs)
model.compile(loss='categorical_crossentropy',
optimizer=Adam(),
metrics=['accuracy'])
model_name = create_name("resnet_three_modules", num_filters)
return (model, model_name)
def build_model_one_module_3x3(dense_units):
input_shape_img = deep_utils.get_rgb_input_shape(IMG_ROWS, IMG_COLUMNS)
block1_units = 48
kernel_shape = (3, 3)
model = Sequential()
model.add(
Conv2D(block1_units,
kernel_size=kernel_shape,
activation='relu',
input_shape=input_shape_img,
name='input_layer'))
model.add(
Conv2D(block1_units,
kernel_size=kernel_shape,
activation='relu',
padding='same'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(dense_units, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(NUM_CLASSES, activation='softmax', name='logits_layer'))
model.compile(loss=keras.losses.categorical_crossentropy,
optimizer=keras.optimizers.Adam(),
metrics=['accuracy'])
model_name = "vgg_one_module_3x3_dense{}".format(dense_units)
return (model, model_name)
def build_model_one_module(n_filters):
input_shape_img = deep_utils.get_rgb_input_shape(IMG_ROWS, IMG_COLUMNS)
inputs = Input(input_shape_img)
x = Conv2D(64, (3, 3), activation='relu', padding='same')(inputs)
x = inception_module(x, n_filters=n_filters)
x = Flatten()(x)
x = Dropout(0.5)(x)
predictions = Dense(2, activation='softmax')(x)
model = Model(input=inputs, output=predictions)
model.compile(loss=keras.losses.categorical_crossentropy,
optimizer=keras.optimizers.Adam(),
metrics=['accuracy'])
model_name = "inception_one_module_" + str(n_filters) + "filters"
return (model, model_name)
