301f593e6d80712315ec965996cdccb9a20b54c0,examples/pretrained_cnn/tutorial_models_squeezenetv1.py,,,#,13

Before Change

import tensorlayer as tl
from tensorlayer.models.imagenet_classes import class_names

tf.logging.set_verbosity(tf.logging.DEBUG)
tl.logging.set_verbosity(tl.logging.DEBUG)

x = tf.placeholder(tf.float32, [None, 224, 224, 3])

// get the whole model
squeezenet = tl.models.SqueezeNetV1(x)

// restore pre-trained parameters
sess = tf.InteractiveSession()

squeezenet.restore_params(sess)

probs = tf.nn.softmax(squeezenet.outputs)

squeezenet.print_params(False)

squeezenet.print_layers()

img1 = tl.vis.read_image("data/tiger.jpeg")
img1 = tl.prepro.imresize(img1, (224, 224))

_ = sess.run(probs, feed_dict={x: [img1]})[0]  // 1st time takes time to compile
start_time = time.time()
prob = sess.run(probs, feed_dict={x: [img1]})[0]
print("  End time : %.5ss" % (time.time() - start_time))
preds = (np.argsort(prob)[::-1])[0:5]
for p in preds:
    print(class_names[p], prob[p])

After Change

img1 = tl.vis.read_image("data/tiger.jpeg")
img1 = tl.prepro.imresize(img1, (224, 224)) / 255
img1 = img1.astype(np.float32)[np.newaxis, ...]

start_time = time.time()
output = squeezenet(img1, is_train=False)
prob = tf.nn.softmax(output)[0].numpy()
print("  End time : %.5ss" % (time.time() - start_time))
preds = (np.argsort(prob)[::-1])[0:5]
for p in preds:

Instances