Move TF Code to Legacy.

README.md

@@ -1,3 +1,3 @@
 # Deep Learning Exercises
 
-This repository includes some deep learning codes for ramp-up, including TensorBasic, TensorCV, TensorNLP, TensorGAN.
+This repository includes some deep learning codes for ramp-up.

tensorflow/TensorBasic/LinearRegression/LinearRegression.py → legacy/tensorflow/TensorBasic/LinearRegression/LinearRegression.py

@@ -1,37 +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])
+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])