79 lines
2.2 KiB
Python
79 lines
2.2 KiB
Python
"""
|
|
related to CNN_mask
|
|
"""
|
|
|
|
from keras.models import Sequential
|
|
from keras.layers import Dense, Dropout, Flatten, Activation
|
|
from keras.layers import Convolution2D, MaxPooling2D
|
|
from keras.utils import np_utils
|
|
from util import *
|
|
from prepare_data import *
|
|
|
|
|
|
def load_mask_data():
|
|
Xy_list = list()
|
|
seg_im_data = load_seg_im_data('seg_im_data.mat')
|
|
Xy = gen_mask_example(seg_im_data)
|
|
np.random.shuffle(Xy)
|
|
X = Xy[:, 2500:]
|
|
y = Xy[:, 0:2500]
|
|
X = X.reshape(X.shape[0], WINDOW_SHAPE[0], WINDOW_SHAPE[1])
|
|
return X, y
|
|
|
|
|
|
BATCH_SIZE = 100
|
|
NB_EPOCH = 10
|
|
NB_OUTPUT = 2500
|
|
img_rows, img_cols = (50, 50)
|
|
INPUT_SHAPE = (img_rows, img_cols, 1)
|
|
MODEL_SAVE_PATH = './CNN_mask.h5'
|
|
|
|
print("loading data...")
|
|
X, y = load_mask_data()
|
|
train_size = int(0.9 * X.shape[0])
|
|
X_train = X[0:train_size, :]
|
|
y_train = y[0:train_size]
|
|
X_test = X[train_size:, :]
|
|
y_test = y[train_size:]
|
|
|
|
X_train = X_train.reshape(X_train.shape[0], 1, img_rows, img_cols)
|
|
X_test = X_test.reshape(X_test.shape[0], 1, img_rows, img_cols)
|
|
X_train = X_train.transpose(0, 2, 3, 1)
|
|
X_test = X_test.transpose(0, 2, 3, 1)
|
|
|
|
X_train = X_train.astype('float32')
|
|
X_test = X_test.astype('float32')
|
|
X_train /= 65535.
|
|
X_test /= 65535.
|
|
print('X_train shape:', X_train.shape)
|
|
print(X_train.shape[0], 'train samples')
|
|
print(X_test.shape[0], 'test samples')
|
|
Y_train = y_train
|
|
Y_test = y_test
|
|
|
|
# define the CNN
|
|
model = Sequential()
|
|
model.add(Convolution2D(nb_filter=16, nb_row=3, nb_col=3, border_mode='valid', input_shape=INPUT_SHAPE))
|
|
model.add(Activation('relu'))
|
|
model.add(MaxPooling2D(pool_size=(2, 2)))
|
|
model.add(Convolution2D(nb_filter=32, nb_row=3, nb_col=3, border_mode='same'))
|
|
model.add(Activation('relu'))
|
|
model.add(MaxPooling2D(pool_size=(2, 2)))
|
|
model.add(Convolution2D(nb_filter=32, nb_row=3, nb_col=3, border_mode='same'))
|
|
model.add(Activation('relu'))
|
|
model.add(MaxPooling2D(pool_size=(2, 2)))
|
|
model.add(Dropout(0.25))
|
|
model.add(Flatten())
|
|
model.add(Dense(64))
|
|
model.add(Activation('relu'))
|
|
model.add(Dropout(0.5))
|
|
model.add(Dense(NB_OUTPUT))
|
|
model.add(Activation('tanh'))
|
|
|
|
model.compile(optimizer='adam', loss='mean_squared_error')
|
|
|
|
model.fit(X_train, Y_train, batch_size=BATCH_SIZE, nb_epoch=NB_EPOCH, shuffle=True,
|
|
verbose=1, validation_split=0.1)
|
|
|
|
model.save(MODEL_SAVE_PATH)
|