cnn_lstm_otc_ocr.py 文件源码

def _batch_norm(self, name, x):
        """Batch normalization."""
        with tf.variable_scope(name):
            params_shape = [x.get_shape()[-1]]

            beta = tf.get_variable(
                'beta', params_shape, tf.float32,
                initializer=tf.constant_initializer(0.0, tf.float32))
            gamma = tf.get_variable(
                'gamma', params_shape, tf.float32,
                initializer=tf.constant_initializer(1.0, tf.float32))

            if self.mode == 'train':
                mean, variance = tf.nn.moments(x, [0, 1, 2], name='moments')

                moving_mean = tf.get_variable(
                    'moving_mean', params_shape, tf.float32,
                    initializer=tf.constant_initializer(0.0, tf.float32),
                    trainable=False)
                moving_variance = tf.get_variable(
                    'moving_variance', params_shape, tf.float32,
                    initializer=tf.constant_initializer(1.0, tf.float32),
                    trainable=False)

                self._extra_train_ops.append(moving_averages.assign_moving_average(
                    moving_mean, mean, 0.9))
                self._extra_train_ops.append(moving_averages.assign_moving_average(
                    moving_variance, variance, 0.9))
            else:
                mean = tf.get_variable(
                    'moving_mean', params_shape, tf.float32,
                    initializer=tf.constant_initializer(0.0, tf.float32),
                    trainable=False)
                variance = tf.get_variable(
                    'moving_variance', params_shape, tf.float32,
                    initializer=tf.constant_initializer(1.0, tf.float32),
                    trainable=False)

                tf.summary.histogram(mean.op.name, mean)
                tf.summary.histogram(variance.op.name, variance)
            # elipson used to be 1e-5. Maybe 0.001 solves NaN problem in deeper net.
            x_bn = tf.nn.batch_normalization(x, mean, variance, beta, gamma, 0.001)
            x_bn.set_shape(x.get_shape())

            return x_bn