def make_simplenet(model_path): cnn = Model(model_path) cnn.build_graph( image_shape=(32, 32, 1), n_classes=10, layers=[ Conv2D(64, (3, 3), activation='relu', batch_normal=0.95), #1 Conv2D(32, (3, 3), activation='relu', batch_normal=0.95), #2 Conv2D(32, (3, 3), activation='relu', batch_normal=0.95), #3 Conv2D(32, (3, 3), activation='relu', batch_normal=0.95), #4 MaxPooling2D((2, 2)), Conv2D(32, (3, 3), activation='relu', batch_normal=0.95), #5 Conv2D(32, (3, 3), activation='relu', batch_normal=0.95), #6 Conv2D(64, (3, 3), activation='relu', batch_normal=0.95), #7 MaxPooling2D((2, 2)), Conv2D(64, (3, 3), activation='relu', batch_normal=0.95), #8 Conv2D(64, (3, 3), activation='relu', batch_normal=0.95), #9 MaxPooling2D((2, 2)), Conv2D(128, (3, 3), activation='relu', batch_normal=0.95), #10 Conv2D(256, (1, 1), activation='relu', batch_normal=0.95), #11 Conv2D(64, (1, 1), activation='relu', batch_normal=0.95), #12 MaxPooling2D((2, 2)), Conv2D(64, (3, 3), activation='relu', batch_normal=0.95), #13 MaxPooling2D((2, 2)), Flatten(), Dense(10), ], alpha=1e-3, ) return cnn
def make_lr(model_path): lr = Model(model_path) lr.build_graph(image_shape=(32, 32, 1), n_classes=10, layers=[ Flatten(), Dense(10), ], alpha=1e-3) return lr
def make_nn(model_path): nn = Model(model_path) nn.build_graph( image_shape=(32, 32, 1), n_classes=10, layers=[ Flatten(), Dense(32 * 32, activation='relu'), Dense(10), ], alpha=1e-4, ) return nn
def make_simplenet_dropout(model_path): cnn = Model(model_path) cnn.build_graph( image_shape=(32, 32, 1), n_classes=10, layers=[ Conv2D(64, (3, 3), activation='relu', batch_normal=0.95), #1 Dropout(0.8), Conv2D(32, (3, 3), activation='relu', batch_normal=0.95), #2 Dropout(0.8), Conv2D(32, (3, 3), activation='relu', batch_normal=0.95), #3 Dropout(0.8), Conv2D(32, (3, 3), activation='relu', batch_normal=0.95), #4 Dropout(0.8), MaxPooling2D((2, 2)), Conv2D(32, (3, 3), activation='relu', batch_normal=0.95), #5 Dropout(0.8), Conv2D(32, (3, 3), activation='relu', batch_normal=0.95), #6 Dropout(0.8), Conv2D(64, (3, 3), activation='relu', batch_normal=0.95), #7 Dropout(0.8), MaxPooling2D((2, 2)), Conv2D(64, (3, 3), activation='relu', batch_normal=0.95), #8 Dropout(0.8), Conv2D(64, (3, 3), activation='relu', batch_normal=0.95), #9 Dropout(0.8), MaxPooling2D((2, 2)), Conv2D(128, (3, 3), activation='relu', batch_normal=0.95), #10 Dropout(0.8), Conv2D(256, (1, 1), activation='relu', batch_normal=0.95), #11 Dropout(0.8), Conv2D(64, (1, 1), activation='relu', batch_normal=0.95), #12 Dropout(0.8), MaxPooling2D((2, 2)), Conv2D(64, (3, 3), activation='relu', batch_normal=0.95), #13 Dropout(0.8), MaxPooling2D((2, 2)), Flatten(), Dense(10), ], alpha=1e-3, ) return cnn
def make_cnn(model_path): cnn = Model(model_path) cnn.build_graph( image_shape=(32, 32, 1), n_classes=10, layers=[ Conv2D(32, (3, 3), activation='relu'), BatchNorm(), MaxPooling2D((2, 2)), Flatten(), Dense(128, activation='relu'), Dropout(0.5), Dense(10), ], alpha=1e-3, ) return cnn