update

01_tensorflow_basic.py

@@ -36,8 +36,8 @@
 y = tf.placeholder(tf.float32, shape=[None, 2])
 
 # weights and bias are the variables to be trained
-weights = tf.Variable(tf.random_normal([6, 2]))
-bias = tf.Variable(tf.zeros([2]))
+weights = tf.Variable(tf.random_normal([6, 2]), name='weights')
+bias = tf.Variable(tf.zeros([2]), name='bias')
 y_pred = tf.nn.softmax(tf.matmul(X, weights) + bias)
 
 # Minimise cost using cross entropy
@@ -61,7 +61,7 @@
 # use session to run the calculation
 with tf.Session() as sess:
     # variables have to be initialized at the first place
-    tf.initialize_all_variables().run()
+    tf.global_variables_initializer().run()
 
     # training loop
     for epoch in range(10):

02_tensorflow_advanced.py

@@ -50,8 +50,8 @@
     y_pred = tf.nn.softmax(tf.matmul(X, weights) + bias)
 
     # add histogram summaries for weights, view on tensorboard
-    tf.histogram_summary('weights', weights)
-    tf.histogram_summary('bias', bias)
+    tf.summary.histogram('weights', weights)
+    tf.summary.histogram('bias', bias)
 
 # Minimise cost using cross entropy
 # NOTE: add a epsilon(1e-10) when calculate log(y_pred),
@@ -60,7 +60,7 @@
     cross_entropy = - tf.reduce_sum(y_true * tf.log(y_pred + 1e-10),
                                     reduction_indices=1)
     cost = tf.reduce_mean(cross_entropy)
-    tf.scalar_summary('loss', cost)
+    tf.summary.scalar('loss', cost)
 
 # use gradient descent optimizer to minimize cost
 train_op = tf.train.GradientDescentOptimizer(0.001).minimize(cost)
@@ -69,7 +69,7 @@
     correct_pred = tf.equal(tf.argmax(y_true, 1), tf.argmax(y_pred, 1))
     acc_op = tf.reduce_mean(tf.cast(correct_pred, tf.float32))
     # Add scalar summary for accuracy
-    tf.scalar_summary('accuracy', acc_op)
+    tf.summary.scalar('accuracy', acc_op)
 
 global_step = tf.Variable(0, name='global_step', trainable=False)
 # use saver to save and restore model
@@ -89,11 +89,11 @@
 # use session to run the calculation
 with tf.Session() as sess:
     # create a log writer. run 'tensorboard --logdir=./logs'
-    writer = tf.train.SummaryWriter('./logs', sess.graph)
-    merged = tf.merge_all_summaries()
+    writer = tf.summary.FileWriter('./logs', sess.graph)
+    merged = tf.summary.merge_all()
 
     # variables have to be initialized at the first place
-    tf.initialize_all_variables().run()
+    tf.global_variables_initializer().run()
 
     # restore variables from checkpoint if exists
     ckpt = tf.train.get_checkpoint_state(ckpt_dir)
@@ -125,7 +125,6 @@
         global_step.assign(epoch).eval()
         saver.save(sess, ckpt_dir + '/logistic.ckpt',
                    global_step=global_step)
-
     print('Training complete!')
 
 ################################

csv_to_tfrecords.py

@@ -0,0 +1,37 @@
+#! -*- coding:utf-8 -*-
+
+import pandas as pd
+import tensorflow as tf
+
+
+# convert train.csv to train.tfrecords
+def transform_to_tfrecord():
+    data = pd.read_csv('data/train.csv')
+    tfrecord_file = 'train.tfrecords'
+
+    def int_feature(value):
+        return tf.train.Feature(
+            int64_list=tf.train.Int64List(value=[value]))
+
+    def float_feature(value):
+        return tf.train.Feature(
+            float_list=tf.train.FloatList(value=[value]))
+
+    writer = tf.python_io.TFRecordWriter(tfrecord_file)
+    for i in range(len(data)):
+        features = tf.train.Features(feature={
+            'Age': float_feature(data['Age'][i]),
+            'Survived': int_feature(data['Survived'][i]),
+            'Pclass': int_feature(data['Pclass'][i]),
+            'Parch': int_feature(data['Parch'][i]),
+            'SibSp': int_feature(data['SibSp'][i]),
+            'Sex': int_feature(1 if data['Sex'][i] == 'male' else 0),
+            'Fare': float_feature(data['Fare'][i])
+        })
+        example = tf.train.Example(features=features)
+        writer.write(example.SerializeToString())
+    writer.close()
+
+
+if __name__ == '__main__':
+    transform_to_tfrecord()