def build_network(): network = resnet_model.resnet_v1( resnet_depth=params['resnet_depth'], num_classes=params['num_label_classes'], dropblock_size=params['dropblock_size'], dropblock_keep_probs=dropblock_keep_probs, data_format=params['data_format']) return network(inputs=features, is_training=(mode == tf.estimator.ModeKeys.TRAIN))
def test_load_resnet18(self): network = resnet_model.resnet_v1( resnet_depth=18, num_classes=10, data_format='channels_last') input_bhw3 = tf.placeholder(tf.float32, [1, 28, 28, 3]) resnet_output = network(inputs=input_bhw3, is_training=True) sess = tf.Session() sess.run(tf.global_variables_initializer()) _ = sess.run( resnet_output, feed_dict={input_bhw3: np.random.randn(1, 28, 28, 3)})