gmm_main.py 文件源码

def neural_network(self, X):
    """pi, mu, sigma = NN(x; theta)"""
    X_image = tf.reshape(X, [-1,IMAGE_ROWS,IMAGE_COLS,1])
    conv1 = Convolution2D(32, 5, 5, border_mode='same', activation=K.relu, W_regularizer=l2(0.01),
                          input_shape=(IMAGE_ROWS, IMAGE_COLS, 1))(X_image)
    pool1 = MaxPooling2D(pool_size=(2,2), border_mode='same')(conv1)
    conv2 = Convolution2D(64, 5, 5, border_mode='same', activation=K.relu, W_regularizer=l2(0.01))(pool1)
    pool2 = MaxPooling2D(pool_size=(2,2), border_mode='same')(conv2)
    pool2_flat = tf.reshape(pool2, [-1, IMAGE_ROWS//4 * IMAGE_COLS//4 * 64])
    hidden1 = Dense(1024, W_regularizer=l2(0.01), activation=K.relu)(pool2_flat)
    hidden2 = Dense(64, W_regularizer=l2(0.01), activation=K.relu)(hidden1)
    self.mus = Dense(self.K)(hidden2)
    self.sigmas = Dense(self.K, activation=K.softplus)(hidden2)
    self.pi = Dense(self.K, activation=K.softmax)(hidden2)