model.py 文件源码

def samples_generator(self, batch_size):
        Z = tf.placeholder(tf.float32, [batch_size, self.dim_z])
        Y = tf.placeholder(tf.float32, [batch_size, self.dim_y])

        yb = tf.reshape(Y, [batch_size, 1, 1, self.dim_y])
        Z_ = tf.concat([Z,Y], 1)
        h1 = tf.nn.relu(batchnormalize(tf.matmul(Z_, self.gen_W1)))
        h1 = tf.concat([h1, Y], 1)
        h2 = tf.nn.relu(batchnormalize(tf.matmul(h1, self.gen_W2)))
        h2 = tf.reshape(h2, [batch_size,6,6,self.dim_W2])
        h2 = tf.concat([h2, yb*tf.ones([batch_size, 6,6, self.dim_y])], 3)

        output_shape_l3 = [batch_size,12,12,self.dim_W3]
        h3 = tf.nn.conv2d_transpose(h2, self.gen_W3, output_shape=output_shape_l3, strides=[1,2,2,1])
        h3 = tf.nn.relu( batchnormalize(h3) )
        h3 = tf.concat([h3, yb*tf.ones([batch_size, 12,12,self.dim_y])], 3)

        output_shape_l4 = [batch_size,24,24,self.dim_channel]
        h4 = tf.nn.conv2d_transpose(h3, self.gen_W4, output_shape=output_shape_l4, strides=[1,2,2,1])
        x = tf.nn.sigmoid(h4)
        return Z, Y, x