Update Linear Regression.

Mansterteddy · Apr 27, 2018 · 04c13d3 · 04c13d3
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diff --git a/CIFAR/config.py → pytorch/CIFAR/config.py b/CIFAR/config.py → pytorch/CIFAR/config.py
diff --git a/CIFAR/main.py → pytorch/CIFAR/main.py b/CIFAR/main.py → pytorch/CIFAR/main.py
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diff --git a/Pytorch MNIST Handler/README.md → pytorch/Pytorch MNIST Handler/README.md b/Pytorch MNIST Handler/README.md → pytorch/Pytorch MNIST Handler/README.md
diff --git a/Pytorch MNIST Handler/py3_MNIST_Handler.py → ...ytorch MNIST Handler/py3_MNIST_Handler.py b/Pytorch MNIST Handler/py3_MNIST_Handler.py → ...ytorch MNIST Handler/py3_MNIST_Handler.py
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diff --git a/style_transfer/main.py → pytorch/style_transfer/main.py b/style_transfer/main.py → pytorch/style_transfer/main.py
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diff --git a/tensorflow/TensorBasic/LinearRegression/LinearRegression.py b/tensorflow/TensorBasic/LinearRegression/LinearRegression.py
@@ -0,0 +1,37 @@
+import matplotlib.pyplot as plt
+import numpy as np
+import tensorflow as tf
+
+x_data = np.random.rand(100).astype(np.float32)
+y_data = 3.0 * x_data + 1.0
+print("x_data: ", x_data)
+print("y_data: ", y_data)
+
+# Define trainable variables
+W = tf.Variable(tf.random_uniform([1], -1.0, 1.0))
+b = tf.Variable(tf.zeros([1]))
+
+# Define graph operations
+y = tf.multiply(W, x_data) + b
+
+# Define loss
+loss = tf.reduce_mean(tf.square(y - y_data))
+
+# Define optimizer for training
+train_optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.5).minimize(loss)
+
+# Define the operation that initializes variables
+init = tf.global_variables_initializer()
+
+# Launch the graph
+with tf.Session() as sess:
+    # Initialization
+    sess.run(init)
+
+    # Start training
+    training_iters = 1000
+    for step in range(training_iters):
+        if step % 20 == 0 or (step + 1)==training_iters:
+            print(step, sess.run(W), sess.run(b))
+
+        _ = sess.run([train_optimizer])