update tensorflow dataset tutorial example

README.md

@@ -29,6 +29,7 @@ All methods mentioned below have their video and text tutorial in Chinese. Visit
   * [Save and reload](https://github.com/MorvanZhou/Tensorflow-Tutorial/blob/master/tutorial-contents/303_save_reload.py)
   * [Optimizers](https://github.com/MorvanZhou/Tensorflow-Tutorial/blob/master/tutorial-contents/304_optimizer.py)
   * [Tensorboard](https://github.com/MorvanZhou/Tensorflow-Tutorial/blob/master/tutorial-contents/305_tensorboard.py)
+  * [Dataset](https://github.com/MorvanZhou/Tensorflow-Tutorial/blob/master/tutorial-contents/306_dataset.py)
 * Advanced neural network
   * [CNN](https://github.com/MorvanZhou/Tensorflow-Tutorial/blob/master/tutorial-contents/401_CNN.py)
   * [RNN-Classification](https://github.com/MorvanZhou/Tensorflow-Tutorial/blob/master/tutorial-contents/402_RNN_classification.py)

tutorial-contents/306_dataset.py

@@ -0,0 +1,48 @@
+"""
+Know more, visit my Python tutorial page: https://morvanzhou.github.io/tutorials/
+My Youtube Channel: https://www.youtube.com/user/MorvanZhou
+
+More information about Dataset: https://github.com/tensorflow/tensorflow/blob/master/tensorflow/docs_src/programmers_guide/datasets.md
+"""
+import tensorflow as tf
+import numpy as np
+from tensorflow.contrib.data import Dataset
+
+
+# load your data or create your data in here
+npx = np.random.uniform(-1, 1, (1000, 1))                           # x data
+npy = np.power(npx, 2) + np.random.normal(0, 0.1, size=npx.shape)   # y data
+npx_train, npx_test = np.split(npx, [800])                          # training and test data
+npy_train, npy_test = np.split(npy, [800])
+
+# use placeholder, later you may need different data, pass the different data into placeholder
+tfx = tf.placeholder(npx_train.dtype, npx_train.shape)
+tfy = tf.placeholder(npy_train.dtype, npy_train.shape)
+
+# create dataloader
+dataset = Dataset.from_tensor_slices((tfx, tfy))
+dataset = dataset.shuffle(buffer_size=1000)   # choose data randomly from this buffer
+dataset = dataset.batch(32)                   # batch size you will use
+dataset = dataset.repeat(3)                   # repeat for 3 epochs
+iterator = dataset.make_initializable_iterator()  # later we have to initialize this one
+
+# your network
+bx, by = iterator.get_next()                  # use batch to update
+l1 = tf.layers.dense(bx, 10, tf.nn.relu)
+out = tf.layers.dense(l1, npy.shape[1])
+loss = tf.losses.mean_squared_error(by, out)
+train = tf.train.GradientDescentOptimizer(0.1).minimize(loss)
+
+sess = tf.Session()
+# need to initialize the iterator in this case
+sess.run([iterator.initializer, tf.global_variables_initializer()], feed_dict={tfx: npx_train, tfy: npy_train})
+
+for step in range(201):
+  try:
+    _, trainl = sess.run([train, loss])                       # train
+    if step % 10 == 0:
+      testl = sess.run(loss, {bx: npx_test, by: npy_test})    # test
+      print('step: %i/200' % step, '|train loss:', trainl, '|test loss:', testl)
+  except tf.errors.OutOfRangeError:     # if training takes more than 3 epochs, training will be stopped
+    print('Finish the last epoch.')
+    break