Fix bugs in examples. (#383)

* fixed-bug-example

* Update tutorial_cifar10_tfrecord.py

* fixed yapf

* fixed yapf

Author: zsdonghao

example/tutorial_cifar10_tfrecord.py

@@ -44,21 +44,15 @@
 
 # import numpy as np
 import tensorflow as tf
-
 import tensorlayer as tl
 from tensorlayer.layers import *
 
-model_file_name = "model_cifar10_tfrecord.ckpt"
+model_file_name = "./model_cifar10_tfrecord.ckpt"
 resume = False  # load model, resume from previous checkpoint?
 
 ## Download data, and convert to TFRecord format, see ```tutorial_tfrecord.py```
 X_train, y_train, X_test, y_test = tl.files.load_cifar10_dataset(shape=(-1, 32, 32, 3), plotable=False)
 
-# X_train = np.asarray(X_train, dtype=np.float32)
-# y_train = np.asarray(y_train, dtype=np.int64)
-# X_test = np.asarray(X_test, dtype=np.float32)
-# y_test = np.asarray(y_test, dtype=np.int64)
-
 print('X_train.shape', X_train.shape)  # (50000, 32, 32, 3)
 print('y_train.shape', y_train.shape)  # (50000,)
 print('X_test.shape', X_test.shape)  # (10000, 32, 32, 3)
@@ -171,7 +165,7 @@ def read_and_decode(filename, is_train=None):
 #     sess.close()
 
 batch_size = 128
-model_file_name = "model_cifar10_advanced.ckpt"
+model_file_name = "./model_cifar10_advanced.ckpt"
 resume = False  # load model, resume from previous checkpoint?
 
 with tf.device('/cpu:0'):
@@ -196,7 +190,7 @@ def model(x_crop, y_, reuse):
             net = Conv2d(net, 64, (5, 5), (1, 1), act=tf.nn.relu, padding='SAME', W_init=W_init, name='cnn1')
             # net = Conv2dLayer(net, act=tf.nn.relu, shape=[5, 5, 3, 64],
             #             strides=[1, 1, 1, 1], padding='SAME',                 # 64 features for each 5x5x3 patch
-            #             W_init=W_init, name ='cnn1')           # output: (batch_size, 24, 24, 64)
+            #             W_init=W_init, name ='cnn1')                          # output: (batch_size, 24, 24, 64)
             net = MaxPool2d(net, (3, 3), (2, 2), padding='SAME', name='pool1')
             # net = PoolLayer(net, ksize=[1, 3, 3, 1], strides=[1, 2, 2, 1],
             #             padding='SAME', pool = tf.nn.max_pool, name ='pool1',)# output: (batch_size, 12, 12, 64)
@@ -207,7 +201,7 @@ def model(x_crop, y_, reuse):
             net = Conv2d(net, 64, (5, 5), (1, 1), act=tf.nn.relu, padding='SAME', W_init=W_init, name='cnn2')
             # net = Conv2dLayer(net, act=tf.nn.relu, shape=[5, 5, 64, 64],
             #             strides=[1, 1, 1, 1], padding='SAME',                 # 64 features for each 5x5 patch
-            #             W_init=W_init, name ='cnn2')           # output: (batch_size, 12, 12, 64)
+            #             W_init=W_init, name ='cnn2')                          # output: (batch_size, 12, 12, 64)
             net = LocalResponseNormLayer(net, depth_radius=4, bias=1.0, alpha=0.001 / 9.0, beta=0.75, name='norm2')
             # net.outputs = tf.nn.lrn(net.outputs, 4, bias=1.0, alpha=0.001 / 9.0,
             #             beta=0.75, name='norm2')
@@ -217,8 +211,7 @@ def model(x_crop, y_, reuse):
             net = FlattenLayer(net, name='flatten')  # output: (batch_size, 2304)
             net = DenseLayer(net, n_units=384, act=tf.nn.relu, W_init=W_init2, b_init=b_init2, name='d1relu')  # output: (batch_size, 384)
             net = DenseLayer(net, n_units=192, act=tf.nn.relu, W_init=W_init2, b_init=b_init2, name='d2relu')  # output: (batch_size, 192)
-            net = DenseLayer(
-                net, n_units=10, act=tf.identity, W_init=tf.truncated_normal_initializer(stddev=1 / 192.0), name='output')  # output: (batch_size, 10)
+            net = DenseLayer(net, n_units=10, act=tf.identity, W_init=W_init2, name='output')  # output: (batch_size, 10)
             y = net.outputs
 
             ce = tl.cost.cross_entropy(y, y_, name='cost')
@@ -264,8 +257,7 @@ def model_batch_norm(x_crop, y_, reuse, is_train):
             net = FlattenLayer(net, name='flatten')  # output: (batch_size, 2304)
             net = DenseLayer(net, n_units=384, act=tf.nn.relu, W_init=W_init2, b_init=b_init2, name='d1relu')  # output: (batch_size, 384)
             net = DenseLayer(net, n_units=192, act=tf.nn.relu, W_init=W_init2, b_init=b_init2, name='d2relu')  # output: (batch_size, 192)
-            net = DenseLayer(
-                net, n_units=10, act=tf.identity, W_init=tf.truncated_normal_initializer(stddev=1 / 192.0), name='output')  # output: (batch_size, 10)
+            net = DenseLayer(net, n_units=10, act=tf.identity, W_init=W_init2, name='output')  # output: (batch_size, 10)
             y = net.outputs
 
             ce = tl.cost.cross_entropy(y, y_, name='cost')
@@ -302,7 +294,7 @@ def model_batch_norm(x_crop, y_, reuse, is_train):
     n_step = n_epoch * n_step_epoch
 
     with tf.device('/gpu:0'):  # <-- remove it if you don't have GPU
-        train_op = tf.train.AdamOptimizer(learning_rate, beta1=0.9, beta2=0.999, epsilon=1e-08, use_locking=False).minimize(cost)
+        train_op = tf.train.AdamOptimizer(learning_rate).minimize(cost)
 
     tl.layers.initialize_global_variables(sess)
     if resume:
@@ -352,6 +344,10 @@ def model_batch_norm(x_crop, y_, reuse, is_train):
             print("Save model " + "!" * 10)
             saver = tf.train.Saver()
             save_path = saver.save(sess, model_file_name)
+            # you can also save model into npz
+            tl.files.save_npz(network.all_params, name='model.npz', sess=sess)
+            # and restore it as follow:
+            # tl.files.load_and_assign_npz(sess=sess, name='model.npz', network=network)
 
     coord.request_stop()
     coord.join(threads)