convnet.py 文件源码

def fc_relu(self, input_tensor, num_outputs, relu=False, batch_norm=False, weight_std=0.005,
                bias_init_value=0.1, name=None):
        if batch_norm and not relu:
            raise ValueError('Cannot use batch normalization without following RELU')
        with tf.variable_scope(name) as scope:
            num_inputs = int(np.prod(input_tensor.get_shape()[1:]))
            w, b = self.get_fc_weights(num_inputs, num_outputs,
                                       weight_std=weight_std,
                                       bias_init_value=bias_init_value)

            fc_relu = None
            input_tensor_reshaped = tf.reshape(input_tensor, [-1, num_inputs])
            fc = tf.add(tf.matmul(input_tensor_reshaped, w), b, name='fc' if relu or batch_norm else name)
            if batch_norm:
                fc = tf.cond(self.is_phase_train,
                             lambda: tflayers.batch_norm(fc,
                                                           decay=self.batch_norm_decay,
                                                           is_training=True,
                                                           trainable=True,
                                                           reuse=None,
                                                           scope=scope),
                              lambda: tflayers.batch_norm(fc,
                                                           decay=self.batch_norm_decay,
                                                           is_training=False,
                                                           trainable=True,
                                                           reuse=True,
                                                           scope=scope))
            if relu:
                fc_relu = tf.nn.relu(fc, name=name)
        return fc, fc_relu