From 16645167e91e2fafe0bd2d27019b1b7ab6c1f08e Mon Sep 17 00:00:00 2001
From: Wan Youwen <1756429307@qq.com>
Date: Wed, 2 May 2018 17:23:55 +0800
Subject: [PATCH] Create 01_logisticRegression.py

---
 deepLearning/01_logisticRegression.py | 96 +++++++++++++++++++++++++++
 1 file changed, 96 insertions(+)
 create mode 100644 deepLearning/01_logisticRegression.py

diff --git a/deepLearning/01_logisticRegression.py b/deepLearning/01_logisticRegression.py
new file mode 100644
index 00000000..bc631d37
--- /dev/null
+++ b/deepLearning/01_logisticRegression.py
@@ -0,0 +1,96 @@
+"""
+Logistic Regression
+author: Ye Hu
+2016/12/14  update 2017/02/16
+redit：wanyouwen 2018/05/02
+"""
+import numpy as np
+import tensorflow as tf
+import input_data
+
+class LogisticRegression(object):
+    """Multi-class logistic regression class"""
+    def __init__(self, inpt, n_in, n_out):
+        """
+        inpt: tf.Tensor, (one minibatch) [None, n_in]
+        n_in: int, number of input units
+        n_out: int, number of output units
+        """
+        # weight
+        self.W = tf.Variable(tf.zeros([n_in, n_out], dtype=tf.float32))
+        # bias
+        self.b = tf.Variable(tf.zeros([n_out,]), dtype=tf.float32)
+        # activation output
+        self.output = tf.nn.softmax(tf.matmul(inpt, self.W) + self.b)
+        # prediction
+        self.y_pred = tf.argmax(self.output, axis=1)
+        # keep track of variables
+        self.params = [self.W, self.b]
+
+    def cost(self, y):
+        """
+        y: tf.Tensor, the target of the input
+        """
+        # cross_entropy
+        return -tf.reduce_mean(tf.reduce_sum(y * tf.log(self.output), axis=1))
+
+    def accuarcy(self, y):
+        """errors"""
+        correct_pred = tf.equal(self.y_pred, tf.argmax(y, axis=1))
+        return tf.reduce_mean(tf.cast(correct_pred, tf.float32))
+
+
+
+
+if __name__ == "__main__":
+    # Load mnist dataset
+    mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
+    # Define placeholder for input and target
+    x = tf.placeholder(tf.float32, shape=[None, 784])
+    y_ = tf.placeholder(tf.float32, shape=[None, 10])
+
+    # Construct model
+    classifier = LogisticRegression(x, n_in=784, n_out=10)
+    cost = classifier.cost(y_)
+    accuracy = classifier.accuarcy(y_)
+    predictor = classifier.y_pred
+    # Define the train operation
+    train_op = tf.train.GradientDescentOptimizer(learning_rate=0.01).minimize(
+        cost, var_list=classifier.params)
+
+    # Initialize all variables
+    init = tf.global_variables_initializer()
+
+    # Training settings
+    training_epochs = 50
+    batch_size = 100
+    display_step = 5
+
+    # Train loop
+    print("Start to train...")
+    with tf.Session() as sess:
+        sess.run(init)
+        for epoch in range(training_epochs):
+            avg_cost = 0.0
+            batch_num = int(mnist.train.num_examples/batch_size)
+            for i in range(batch_num):
+                x_batch, y_batch = mnist.train.next_batch(batch_size)
+                # Run train op
+                c, _ = sess.run([cost, train_op], feed_dict={x: x_batch, y_: y_batch})
+                # Sum up cost
+                avg_cost += c/batch_num
+
+            if epoch % display_step == 0:
+                val_acc = sess.run(accuracy, feed_dict={x: mnist.validation.images,
+                                                       y_: mnist.validation.labels})
+                print("Epoch {0} cost: {1}, validation accuacy: {2}".format(epoch,
+                      avg_cost, val_acc))
+
+        print("Finished!")
+        test_x = mnist.test.images[:10]
+        test_y = mnist.test.labels[:10]
+        print("Ture lables:")
+        print("  ", np.argmax(test_y, 1))
+        print("Prediction:")
+        print("  ", sess.run(predictor, feed_dict={x: test_x}))
+