Update lab-09-4-xor_tensorboard.py (#239)

* Update lab-09-4-xor_tensorboard.py

Simplify code and delete unnecessary variables.

* Update lab-09-4-xor_tensorboard.py

Co-Authored-By: qoocrab <qoocrab@gmail.com>

* Update lab-09-4-xor_tensorboard.py

Co-Authored-By: qoocrab <qoocrab@gmail.com>

* Update lab-09-4-xor_tensorboard.py

Co-Authored-By: qoocrab <qoocrab@gmail.com>

* Update lab-09-4-xor_tensorboard.py

Co-Authored-By: qoocrab <qoocrab@gmail.com>

* Update lab-09-4-xor_tensorboard.py

Add f-string output.

lab-09-4-xor_tensorboard.py

@@ -3,55 +3,45 @@
 import numpy as np
 
 tf.set_random_seed(777)  # for reproducibility
-learning_rate = 0.01
-
-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')
-
-with tf.name_scope("layer1"):
-    W1 = tf.Variable(tf.random_normal([2, 2]), name='weight1')
-    b1 = tf.Variable(tf.random_normal([2]), name='bias1')
+
+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 = tf.placeholder(tf.float32, [None, 2], name="x")
+Y = tf.placeholder(tf.float32, [None, 1], name="y")
+
+with tf.name_scope("Layer1"):
+    W1 = tf.Variable(tf.random_normal([2, 2]), name="weight_1")
+    b1 = tf.Variable(tf.random_normal([2]), name="bias_1")
     layer1 = tf.sigmoid(tf.matmul(X, W1) + b1)
 
-    w1_hist = tf.summary.histogram("weights1", W1)
-    b1_hist = tf.summary.histogram("biases1", b1)
-    layer1_hist = tf.summary.histogram("layer1", layer1)
+    tf.summary.histogram("W1", W1)
+    tf.summary.histogram("b1", b1)
+    tf.summary.histogram("Layer1", layer1)
 
 
-with tf.name_scope("layer2"):
-    W2 = tf.Variable(tf.random_normal([2, 1]), name='weight2')
-    b2 = tf.Variable(tf.random_normal([1]), name='bias2')
+with tf.name_scope("Layer2"):
+    W2 = tf.Variable(tf.random_normal([2, 1]), name="weight_2")
+    b2 = tf.Variable(tf.random_normal([1]), name="bias_2")
     hypothesis = tf.sigmoid(tf.matmul(layer1, W2) + b2)
 
-    w2_hist = tf.summary.histogram("weights2", W2)
-    b2_hist = tf.summary.histogram("biases2", b2)
-    hypothesis_hist = tf.summary.histogram("hypothesis", hypothesis)
+    tf.summary.histogram("W2", W2)
+    tf.summary.histogram("b2", b2)
+    tf.summary.histogram("Hypothesis", hypothesis)
 
 # cost/loss function
-with tf.name_scope("cost"):
-    cost = -tf.reduce_mean(Y * tf.log(hypothesis) + (1 - Y) *
-                           tf.log(1 - hypothesis))
-    cost_summ = tf.summary.scalar("cost", cost)
+with tf.name_scope("Cost"):
+    cost = -tf.reduce_mean(Y * tf.log(hypothesis) + (1 - Y) * tf.log(1 - hypothesis))
+    tf.summary.scalar("Cost", cost)
 
-with tf.name_scope("train"):
-    train = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost)
+with tf.name_scope("Train"):
+    train = tf.train.AdamOptimizer(learning_rate=0.01).minimize(cost)
 
 # Accuracy computation
 # True if hypothesis>0.5 else False
 predicted = tf.cast(hypothesis > 0.5, dtype=tf.float32)
 accuracy = tf.reduce_mean(tf.cast(tf.equal(predicted, Y), dtype=tf.float32))
-accuracy_summ = tf.summary.scalar("accuracy", accuracy)
+tf.summary.scalar("accuracy", accuracy)
 
 # Launch graph
 with tf.Session() as sess:
@@ -64,27 +54,32 @@
     sess.run(tf.global_variables_initializer())
 
     for step in range(10001):
-        summary, _ = sess.run([merged_summary, train], feed_dict={X: x_data, Y: y_data})
+        _, summary, cost_val = sess.run(
+            [train, merged_summary, cost], feed_dict={X: x_data, Y: y_data}
+        )
         writer.add_summary(summary, global_step=step)
 
         if step % 100 == 0:
-            print(step, sess.run(cost, feed_dict={
-                  X: x_data, Y: y_data}), sess.run([W1, W2]))
+            print(step, cost_val)
 
     # Accuracy report
-    h, c, a = sess.run([hypothesis, predicted, accuracy],
-                       feed_dict={X: x_data, Y: y_data})
-    print("\nHypothesis: ", h, "\nCorrect: ", c, "\nAccuracy: ", a)
-
-
-'''
-Hypothesis:  [[  6.13103184e-05]
- [  9.99936938e-01]
- [  9.99950767e-01]
- [  5.97514772e-05]]
-Correct:  [[ 0.]
- [ 1.]
- [ 1.]
- [ 0.]]
-Accuracy:  1.0
-'''
+    h, p, a = sess.run(
+        [hypothesis, predicted, accuracy], feed_dict={X: x_data, Y: y_data}
+    )
+
+    print(f"\nHypothesis:\n{h} \nPredicted:\n{p} \nAccuracy:\n{a}")
+
+"""
+Hypothesis:
+[[6.1310326e-05]
+ [9.9993694e-01]
+ [9.9995077e-01]
+ [5.9751470e-05]] 
+Predicted:
+[[0.]
+ [1.]
+ [1.]
+ [0.]] 
+Accuracy:
+1.0
+"""