Tutorial fixing

CHANGELOG.md

@@ -125,7 +125,10 @@ To release a new version, please update the changelog as followed:
 - Error in `tl.layers.TernaryConv2d` fixed - self.inputs not defined (by @DEKHTIARJonathan in #658)
 - Deprecation warning fixed in `tl.layers.binary._compute_threshold()` (by @DEKHTIARJonathan in #658)
 - All references to `tf.logging` replaced by `tl.logging` (by @DEKHTIARJonathan in #661)
-
+- Tutorial:
+  - `tutorial_word2vec_basic.py` saving issue #476 fixed (by @DEKHTIARJonathan in #635)
+  - All tutorials tested and errors have been fixed (by @DEKHTIARJonathan in #635)
+
 ### Security
 
 ### Dependencies Update

example/tutorial_cifar10_tfrecord.py

@@ -150,7 +150,7 @@ def read_and_decode(filename, is_train=None):
 # print("img_batch   : %s" % img_batch._shape)
 # print("label_batch : %s" % label_batch._shape)
 #
-# init = tf.initialize_all_variables()
+# init = tf.global_variables_initializer()
 # with tf.Session() as sess:
 #     sess.run(init)
 #     coord = tf.train.Coordinator()

example/tutorial_frozenlake_dqn.py

@@ -26,7 +26,10 @@
 
 """
 
+import time
 import gym
+import numpy as np
+
 import tensorflow as tf
 import tensorlayer as tl
 from tensorlayer.layers import *

example/tutorial_generate_text.py

@@ -120,7 +120,7 @@ def customized_clean_str(string):
 
 
 def customized_read_words(input_fpath):  #, dictionary):
-    with open(input_fpath, "r") as f:
+    with open(input_fpath, "r", encoding="utf8") as f:
         words = f.read()
     # Clean the data
     words = customized_clean_str(words)
@@ -155,7 +155,7 @@ def main_restore_embedding_layer():
 
     emb_net = tl.layers.EmbeddingInputlayer(x, vocabulary_size, embedding_size, name='emb')
 
-    # sess.run(tf.initialize_all_variables())
+    # sess.run(tf.global_variables_initializer())
     tl.layers.initialize_global_variables(sess)
 
     tl.files.assign_params(sess, [load_params[0]], emb_net)
@@ -369,5 +369,3 @@ def loss_fn(outputs, targets, batch_size, sequence_length):
 
     # How to generate text from a given context
     main_lstm_generate_text()
-
-#

example/tutorial_inceptionV3_tfslim.py

@@ -38,12 +38,14 @@
 
 slim = tf.contrib.slim
 try:
-    from data.imagenet_classes import *
+    from tensorlayer.models.imagenet_classes import *
 except Exception as e:
     raise Exception(
         "{} / download the file from: https://github.com/zsdonghao/tensorlayer/tree/master/example/data".format(e)
     )
 
+MODEL_PATH = os.path.join("models", 'inception_v3.ckpt')
+
 
 def load_image(path):
     # load image
@@ -58,7 +60,7 @@ def load_image(path):
     xx = int((img.shape[1] - short_edge) / 2)
     crop_img = img[yy:yy + short_edge, xx:xx + short_edge]
     # resize to 299, 299
-    resized_img = skimage.transform.resize(crop_img, (299, 299))
+    resized_img = skimage.transform.resize(crop_img, (299, 299), anti_aliasing=False)
     return resized_img
 
 
@@ -89,7 +91,7 @@ def print_prob(prob):
 #                                     name='alexnet_v2'  # <-- the name should be the same with the ckpt model
 #                                     )
 # sess = tf.InteractiveSession()
-# # sess.run(tf.initialize_all_variables())
+# # sess.run(tf.global_variables_initializer())
 # tl.layers.initialize_global_variables(sess)
 # network.print_params()
 
@@ -122,15 +124,15 @@ def print_prob(prob):
 network.print_params(False)
 
 saver = tf.train.Saver()
-if not os.path.isfile("inception_v3.ckpt"):
+if not os.path.isfile(MODEL_PATH):
     raise Exception(
         "Please download inception_v3 ckpt from https://github.com/tensorflow/models/tree/master/research/slim"
     )
 
 try:  # TF12+
-    saver.restore(sess, "./inception_v3.ckpt")
+    saver.restore(sess, MODEL_PATH)
 except Exception:  # TF11
-    saver.restore(sess, "inception_v3.ckpt")
+    saver.restore(sess, MODEL_PATH)
 print("Model Restored")
 
 y = network.outputs

example/tutorial_mobilenet.py

@@ -17,6 +17,8 @@
     BatchNormLayer, Conv2d, DepthwiseConv2d, FlattenLayer, GlobalMeanPool2d, InputLayer, ReshapeLayer
 )
 
+MODEL_PATH = os.path.join("models", "mobilenet.npz")
+
 
 def conv_block(n, n_filter, filter_size=(3, 3), strides=(1, 1), is_train=False, name='conv_block'):
     # ref: https://github.com/keras-team/keras/blob/master/keras/applications/mobilenet.py
@@ -101,10 +103,10 @@ def mobilenet(x, is_train=True, reuse=False):
 sess = tf.InteractiveSession()
 # tl.layers.initialize_global_variables(sess)
 
-if not os.path.isfile("mobilenet.npz"):
+if not os.path.isfile(MODEL_PATH):
     raise Exception("Please download mobilenet.npz from : https://github.com/tensorlayer/pretrained-models")
 
-tl.files.load_and_assign_npz(sess=sess, name='mobilenet.npz', network=n)
+tl.files.load_and_assign_npz(sess=sess, name=MODEL_PATH, network=n)
 
 img = tl.vis.read_image('data/tiger.jpeg')
 img = tl.prepro.imresize(img, (224, 224)) / 255
@@ -114,4 +116,4 @@ def mobilenet(x, is_train=True, reuse=False):
 
 print("  End time : %.5ss" % (time.time() - start_time))
 print('Predicted :', decode_predictions([prob], top=3)[0])
-# tl.files.save_npz(n.all_params, name='mobilenet.npz', sess=sess)
+# tl.files.save_npz(n.all_params, name=MODEL_PATH, sess=sess)

example/tutorial_ptb_lstm.py

@@ -100,17 +100,27 @@
 
 """
 
+import sys
 import time
 
 import numpy as np
 import tensorflow as tf
 
 import tensorlayer as tl
 
-flags = tf.flags
+flags = tf.app.flags
+
 flags.DEFINE_string("model", "small", "A type of model. Possible options are: small, medium, large.")
+
+if (tf.VERSION >= '1.5'):
+    # parse flags
+    flags.FLAGS(sys.argv, known_only=True)
+    flags.ArgumentParser()
+
 FLAGS = flags.FLAGS
 
+tf.logging.set_verbosity(tf.logging.DEBUG)
+
 
 def main(_):
     """
@@ -235,7 +245,7 @@ def inference(x, is_training, num_steps, reuse=None):
     # Inference for Testing (Evaluation)
     net_test, lstm1_test, lstm2_test = inference(input_data_test, is_training=False, num_steps=1, reuse=True)
 
-    # sess.run(tf.initialize_all_variables())
+    # sess.run(tf.global_variables_initializer())
     tl.layers.initialize_global_variables(sess)
 
     def loss_fn(outputs, targets):  #, batch_size, num_steps):
@@ -269,7 +279,7 @@ def loss_fn(outputs, targets):  #, batch_size, num_steps):
     optimizer = tf.train.GradientDescentOptimizer(lr)
     train_op = optimizer.apply_gradients(zip(grads, tvars))
 
-    # sess.run(tf.initialize_all_variables())
+    # sess.run(tf.global_variables_initializer())
     tl.layers.initialize_global_variables(sess)
 
     net.print_params()

example/tutorial_ptb_lstm_state_is_tuple.py

@@ -101,17 +101,27 @@
 
 """
 
+import sys
 import time
 
 import numpy as np
 import tensorflow as tf
 
 import tensorlayer as tl
 
-flags = tf.flags
+flags = tf.app.flags
+
 flags.DEFINE_string("model", "small", "A type of model. Possible options are: small, medium, large.")
+
+if (tf.VERSION >= '1.5'):
+    # parse flags
+    flags.FLAGS(sys.argv, known_only=True)
+    flags.ArgumentParser()
+
 FLAGS = flags.FLAGS
 
+tf.logging.set_verbosity(tf.logging.DEBUG)
+
 
 def main(_):
     """
@@ -241,7 +251,7 @@ def inference(x, is_training, num_steps, reuse=None):
     # Inference for Testing (Evaluation)
     net_test, lstm1_test, lstm2_test = inference(input_data_test, is_training=False, num_steps=1, reuse=True)
 
-    # sess.run(tf.initialize_all_variables())
+    # sess.run(tf.global_variables_initializer())
     tl.layers.initialize_global_variables(sess)
 
     def loss_fn(outputs, targets, batch_size):
@@ -275,7 +285,7 @@ def loss_fn(outputs, targets, batch_size):
     optimizer = tf.train.GradientDescentOptimizer(lr)
     train_op = optimizer.apply_gradients(zip(grads, tvars))
 
-    # sess.run(tf.initialize_all_variables())
+    # sess.run(tf.global_variables_initializer())
     tl.layers.initialize_global_variables(sess)
 
     net.print_params()

example/tutorial_squeezenet.py

@@ -11,6 +11,8 @@
 import tensorlayer as tl
 from tensorlayer.layers import (ConcatLayer, Conv2d, DropoutLayer, GlobalMeanPool2d, InputLayer, MaxPool2d)
 
+MODEL_PATH = os.path.join("models", "squeezenet.npz")
+
 
 def decode_predictions(preds, top=5):  # keras.applications.resnet50
     fpath = os.path.join("data", "imagenet_class_index.json")
@@ -114,8 +116,8 @@ def squeezenet(x, is_train=True, reuse=False):
 sess = tf.InteractiveSession()
 tl.layers.initialize_global_variables(sess)
 
-if tl.files.file_exists('squeezenet.npz'):
-    tl.files.load_and_assign_npz(sess=sess, name='squeezenet.npz', network=n)
+if tl.files.file_exists(MODEL_PATH):
+    tl.files.load_and_assign_npz(sess=sess, name=MODEL_PATH, network=n)
 else:
     raise Exception(
         "please download the pre-trained squeezenet.npz from https://github.com/tensorlayer/pretrained-models"
@@ -129,4 +131,4 @@ def squeezenet(x, is_train=True, reuse=False):
 print("  End time : %.5ss" % (time.time() - start_time))
 
 print('Predicted:', decode_predictions([prob], top=3)[0])
-tl.files.save_npz(n.all_params, name='squeezenet.npz', sess=sess)
+tl.files.save_npz(n.all_params, name=MODEL_PATH, sess=sess)

example/tutorial_tfrecord.py

@@ -98,9 +98,9 @@ def read_and_decode(filename):
 img_batch, label_batch = tf.train.shuffle_batch(
     [img, label], batch_size=4, capacity=2000, min_after_dequeue=1000, num_threads=16
 )
-print("img_batch   : %s" % img_batch._shape)
-print("label_batch : %s" % label_batch._shape)
-# init = tf.initialize_all_variables()
+print("img_batch   : %s" % img_batch.shape)
+print("label_batch : %s" % label_batch.shape)
+# init = tf.global_variables_initializer()
 with tf.Session() as sess:
     # sess.run(init)
     tl.layers.initialize_global_variables(sess)
@@ -116,5 +116,3 @@ def read_and_decode(filename):
     coord.request_stop()
     coord.join(threads)
     sess.close()
-
-#

example/tutorial_tfrecord2.py

@@ -83,9 +83,9 @@ def read_and_decode(filename):
 img_batch, label_batch = tf.train.shuffle_batch([img, label], batch_size=4, capacity=50000, \
     min_after_dequeue=10000, num_threads=1)
 
-print("img_batch   : %s" % img_batch._shape)
-print("label_batch : %s" % label_batch._shape)
-# init = tf.initialize_all_variables()
+print("img_batch   : %s" % img_batch.shape)
+print("label_batch : %s" % label_batch.shape)
+# init = tf.global_variables_initializer()
 with tf.Session() as sess:
     # sess.run(init)
     tl.layers.initialize_global_variables(sess)

example/tutorial_tfrecord3.py

@@ -351,7 +351,7 @@ def prefetch_input_data(
     num_threads=4
 )
 sess = tf.Session()
-# sess.run(tf.initialize_all_variables())
+# sess.run(tf.global_variables_initializer())
 tl.layers.initialize_global_variables(sess)
 coord = tf.train.Coordinator()
 threads = tf.train.start_queue_runners(sess=sess, coord=coord)
@@ -447,7 +447,7 @@ def batch_with_dynamic_pad(images_and_captions, batch_size, queue_capacity, add_
     batch_with_dynamic_pad(images_and_captions=[[img, img_cap]], batch_size=4, queue_capacity=50000)
 )
 sess = tf.Session()
-sess.run(tf.initialize_all_variables())
+sess.run(tf.global_variables_initializer())
 coord = tf.train.Coordinator()
 threads = tf.train.start_queue_runners(sess=sess, coord=coord)
 for _ in range(3):
@@ -460,5 +460,3 @@ def batch_with_dynamic_pad(images_and_captions, batch_size, queue_capacity, add_
 coord.request_stop()
 coord.join(threads)
 sess.close()
-
-#

example/tutorial_vgg16.py

@@ -37,13 +37,17 @@
 """
 
 import os
+import time
+
+import numpy as np
 from scipy.misc import imread, imresize
+
 import tensorflow as tf
 import tensorlayer as tl
 from tensorlayer.layers import *
 
 try:
-    from data.imagenet_classes import *
+    from tensorlayer.models.imagenet_classes import *
 except Exception as e:
     raise Exception(
         "{} / download the file from: https://github.com/zsdonghao/tensorlayer/tree/master/example/data".format(e)
@@ -155,14 +159,14 @@ def fc_layers(net):
 net.print_params()
 net.print_layers()
 
-if not os.path.isfile("vgg16_weights.npz"):
-    print("Please download vgg16_weights.npz from : http://www.cs.toronto.edu/~frossard/post/vgg16/")
-    exit()
-npz = np.load('vgg16_weights.npz')
+tl.files.maybe_download_and_extract(
+    'vgg16_weights.npz', 'models', 'http://www.cs.toronto.edu/~frossard/vgg16/', expected_bytes=553436134
+)
+npz = np.load(os.path.join('models', 'vgg16_weights.npz'))
 
 params = []
 for val in sorted(npz.items()):
-    print("  Loading %s" % str(val[1].shape))
+    print("  Loading params %s" % str(val[1].shape))
     params.append(val[1])
 
 tl.files.assign_params(sess, params, net)