aoguren

with slim.arg_scope([slim.conv2d, slim.fully_connected], activation_fn=tf.nn.relu, biases_initializer=tf.random_normal_initializer, weights_initializer=tf.random_normal_initializer, ): conv1 = slim.conv2d(x, 32, [3, 3], 1) pool1 = slim.max_pool2d(conv1, [2, 2], 2, padding='SAME') drop1 = slim.dropout(pool1, keep_prob=keep_prob) conv2 = slim.conv2d(drop1, 64, [3,...

why add tf.pad operator before slim.conv2d?

1

@hizhangp net = tf.pad(images, np.array([[0, 0], [3, 3], [3, 3], [0, 0]]), name='pad_1') net = slim.conv2d(net, 64, 7, 2, padding='VALID', scope='conv_2') I don't understand why the tf.pad was added before...