modify list indentation style. (hunkim#128)

lab-04-2-multi_variable_matmul_linear_regression.py

@@ -2,9 +2,16 @@
 import tensorflow as tf
 tf.set_random_seed(777)  # for reproducibility
 
-x_data = [[73., 80., 75.], [93., 88., 93.],
-          [89., 91., 90.], [96., 98., 100.], [73., 66., 70.]]
-y_data = [[152.], [185.], [180.], [196.], [142.]]
+x_data = [[73., 80., 75.],
+          [93., 88., 93.],
+          [89., 91., 90.],
+          [96., 98., 100.],
+          [73., 66., 70.]]
+y_data = [[152.],
+          [185.],
+          [180.],
+          [196.],
+          [142.]]
 
 
 # placeholders for a tensor that will be always fed.

lab-05-1-logistic_regression.py

@@ -2,8 +2,18 @@
 import tensorflow as tf
 tf.set_random_seed(777)  # for reproducibility
 
-x_data = [[1, 2], [2, 3], [3, 1], [4, 3], [5, 3], [6, 2]]
-y_data = [[0], [0], [0], [1], [1], [1]]
+x_data = [[1, 2],
+          [2, 3],
+          [3, 1],
+          [4, 3],
+          [5, 3],
+          [6, 2]]
+y_data = [[0],
+          [0],
+          [0],
+          [1],
+          [1],
+          [1]]
 
 # placeholders for a tensor that will be always fed.
 X = tf.placeholder(tf.float32, shape=[None, 2])

lab-06-1-softmax_classifier.py

@@ -2,10 +2,22 @@
 import tensorflow as tf
 tf.set_random_seed(777)  # for reproducibility
 
-x_data = [[1, 2, 1, 1], [2, 1, 3, 2], [3, 1, 3, 4], [4, 1, 5, 5],
-          [1, 7, 5, 5], [1, 2, 5, 6], [1, 6, 6, 6], [1, 7, 7, 7]]
-y_data = [[0, 0, 1], [0, 0, 1], [0, 0, 1], [0, 1, 0],
-          [0, 1, 0], [0, 1, 0], [1, 0, 0], [1, 0, 0]]
+x_data = [[1, 2, 1, 1],
+          [2, 1, 3, 2],
+          [3, 1, 3, 4],
+          [4, 1, 5, 5],
+          [1, 7, 5, 5],
+          [1, 2, 5, 6],
+          [1, 6, 6, 6],
+          [1, 7, 7, 7]]
+y_data = [[0, 0, 1],
+          [0, 0, 1],
+          [0, 0, 1],
+          [0, 1, 0],
+          [0, 1, 0],
+          [0, 1, 0],
+          [1, 0, 0],
+          [1, 0, 0]]
 
 X = tf.placeholder("float", [None, 4])
 Y = tf.placeholder("float", [None, 3])

lab-07-1-learning_rate_and_evaluation.py

@@ -2,15 +2,31 @@
 import tensorflow as tf
 tf.set_random_seed(777)  # for reproducibility
 
-x_data = [[1, 2, 1], [1, 3, 2], [1, 3, 4], [1, 5, 5],
-          [1, 7, 5], [1, 2, 5], [1, 6, 6], [1, 7, 7]]
-y_data = [[0, 0, 1], [0, 0, 1], [0, 0, 1], [0, 1, 0],
-          [0, 1, 0], [0, 1, 0], [1, 0, 0], [1, 0, 0]]
+x_data = [[1, 2, 1],
+          [1, 3, 2],
+          [1, 3, 4],
+          [1, 5, 5],
+          [1, 7, 5],
+          [1, 2, 5],
+          [1, 6, 6],
+          [1, 7, 7]]
+y_data = [[0, 0, 1],
+          [0, 0, 1],
+          [0, 0, 1],
+          [0, 1, 0],
+          [0, 1, 0],
+          [0, 1, 0],
+          [1, 0, 0],
+          [1, 0, 0]]
 
 
 # Evaluation our model using this test dataset
-x_test = [[2, 1, 1], [3, 1, 2], [3, 3, 4]]
-y_test = [[0, 0, 1], [0, 0, 1], [0, 0, 1]]
+x_test = [[2, 1, 1],
+          [3, 1, 2],
+          [3, 3, 4]]
+y_test = [[0, 0, 1],
+          [0, 0, 1],
+          [0, 0, 1]]
 
 X = tf.placeholder("float", [None, 3])
 Y = tf.placeholder("float", [None, 3])

lab-09-1-xor.py

@@ -4,8 +4,16 @@
 import numpy as np
 tf.set_random_seed(777)  # for reproducibility
 
-x_data = np.array([[0, 0], [0, 1], [1, 0], [1, 1]], dtype=np.float32)
-y_data = np.array([[0], [1], [1], [0]], dtype=np.float32)
+x_data = [[0, 0],
+          [0, 1],
+          [1, 0],
+          [1, 1]]
+y_data = [[0],
+          [1],
+          [1],
+          [0]]
+x_data = np.array(x_data, dtype=np.float32)
+y_data = np.array(y_data, dtype=np.float32)
 
 X = tf.placeholder(tf.float32, [None, 2])
 Y = tf.placeholder(tf.float32, [None, 1])

lab-09-2-xor-nn.py

@@ -4,8 +4,16 @@
 import numpy as np
 tf.set_random_seed(777)  # for reproducibility
 
-x_data = np.array([[0, 0], [0, 1], [1, 0], [1, 1]], dtype=np.float32)
-y_data = np.array([[0], [1], [1], [0]], dtype=np.float32)
+x_data = [[0, 0],
+          [0, 1],
+          [1, 0],
+          [1, 1]]
+y_data = [[0],
+          [1],
+          [1],
+          [0]]
+x_data = np.array(x_data, dtype=np.float32)
+y_data = np.array(y_data, dtype=np.float32)
 
 X = tf.placeholder(tf.float32, [None, 2])
 Y = tf.placeholder(tf.float32, [None, 1])

lab-09-3-xor-nn-wide-deep.py

@@ -4,8 +4,16 @@
 import numpy as np
 tf.set_random_seed(777)  # for reproducibility
 
-x_data = np.array([[0, 0], [0, 1], [1, 0], [1, 1]], dtype=np.float32)
-y_data = np.array([[0], [1], [1], [0]], dtype=np.float32)
+x_data = [[0, 0],
+          [0, 1],
+          [1, 0],
+          [1, 1]]
+y_data = [[0],
+          [1],
+          [1],
+          [0]]
+x_data = np.array(x_data, dtype=np.float32)
+y_data = np.array(y_data, dtype=np.float32)
 
 X = tf.placeholder(tf.float32, [None, 2])
 Y = tf.placeholder(tf.float32, [None, 1])

lab-09-4-xor_tensorboard.py

@@ -4,8 +4,16 @@
 import numpy as np
 tf.set_random_seed(777)  # for reproducibility
 
-x_data = np.array([[0, 0], [0, 1], [1, 0], [1, 1]], dtype=np.float32)
-y_data = np.array([[0], [1], [1], [0]], dtype=np.float32)
+x_data = [[0, 0],
+          [0, 1],
+          [1, 0],
+          [1, 1]]
+y_data = [[0],
+          [1],
+          [1],
+          [0]]
+x_data = np.array(x_data, dtype=np.float32)
+y_data = np.array(y_data, dtype=np.float32)
 
 X = tf.placeholder(tf.float32, [None, 2], name='x-input')
 Y = tf.placeholder(tf.float32, [None, 1], name='y-input')

lab-09-5-linear_back_prop.py

@@ -6,8 +6,12 @@
 tf.set_random_seed(777)  # reproducibility
 
 # tf Graph Input
-x_data = [[1.], [2.], [3.]]
-y_data = [[1.], [2.], [3.]]
+x_data = [[1.],
+          [2.],
+          [3.]]
+y_data = [[1.],
+          [2.],
+          [3.]]
 
 
 # placeholders for a tensor that will be always fed.

lab-09-6-multi-linear_back_prop.py

@@ -6,9 +6,16 @@
 tf.set_random_seed(777)  # reproducibility
 
 # tf Graph Input
-x_data = [[73., 80., 75.], [93., 88., 93.],
-          [89., 91., 90.], [96., 98., 100.], [73., 66., 70.]]
-y_data = [[152.], [185.], [180.], [196.], [142.]]
+x_data = [[73., 80., 75.],
+          [93., 88., 93.],
+          [89., 91., 90.],
+          [96., 98., 100.],
+          [73., 66., 70.]]
+y_data = [[152.],
+          [185.],
+          [180.],
+          [196.],
+          [142.]]
 
 # placeholders for a tensor that will be always fed.
 X = tf.placeholder(tf.float32, shape=[None, 3])