Update lab-07-1-learning_rate_and_evaluation.py

1. Modified to a learning rate that is appropriate for learning.
2. The arg_max is modified to argmax.

lab-07-1-learning_rate_and_evaluation.py

@@ -19,7 +19,6 @@
           [1, 0, 0],
           [1, 0, 0]]
 
-
 # Evaluation our model using this test dataset
 x_test = [[2, 1, 1],
           [3, 1, 2],
@@ -41,12 +40,11 @@
 # Cross entropy cost/loss
 cost = tf.reduce_mean(-tf.reduce_sum(Y * tf.log(hypothesis), axis=1))
 # Try to change learning_rate to small numbers
-optimizer = tf.train.GradientDescentOptimizer(
-    learning_rate=1e-10).minimize(cost)
+optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.1).minimize(cost)
 
 # Correct prediction Test model
-prediction = tf.arg_max(hypothesis, 1)
-is_correct = tf.equal(prediction, tf.arg_max(Y, 1))
+prediction = tf.argmax(hypothesis, 1)
+is_correct = tf.equal(prediction, tf.argmax(Y, 1))
 accuracy = tf.reduce_mean(tf.cast(is_correct, tf.float32))
 
 # Launch graph
@@ -55,8 +53,7 @@
     sess.run(tf.global_variables_initializer())
 
     for step in range(201):
-        cost_val, W_val, _ = sess.run(
-            [cost, W, optimizer], feed_dict={X: x_data, Y: y_data})
+        cost_val, W_val, _ = sess.run([cost, W, optimizer], feed_dict={X: x_data, Y: y_data})
         print(step, cost_val, W_val)
 
     # predict
@@ -66,6 +63,7 @@
 
 '''
 when lr = 1.5
+
 0 5.73203 [[-0.30548954  1.22985029 -0.66033536]
  [-4.39069986  2.29670858  2.99386835]
  [-3.34510708  2.09743214 -0.80419564]]
@@ -87,27 +85,19 @@
 6 nan [[ nan  nan  nan]
  [ nan  nan  nan]
  [ nan  nan  nan]]
-
  ...
 Prediction: [0 0 0]
 Accuracy:  0.0
-
 -------------------------------------------------
 When lr = 1e-10
+
 0 5.73203 [[ 0.80269563  0.67861295 -1.21728313]
  [-0.3051686  -0.3032113   1.50825703]
  [ 0.75722361 -0.7008909  -2.10820389]]
 1 5.73203 [[ 0.80269563  0.67861295 -1.21728313]
  [-0.3051686  -0.3032113   1.50825703]
  [ 0.75722361 -0.7008909  -2.10820389]]
-2 5.73203 [[ 0.80269563  0.67861295 -1.21728313]
- [-0.3051686  -0.3032113   1.50825703]
- [ 0.75722361 -0.7008909  -2.10820389]]
 ...
-
-198 5.73203 [[ 0.80269563  0.67861295 -1.21728313]
- [-0.3051686  -0.3032113   1.50825703]
- [ 0.75722361 -0.7008909  -2.10820389]]
 199 5.73203 [[ 0.80269563  0.67861295 -1.21728313]
  [-0.3051686  -0.3032113   1.50825703]
  [ 0.75722361 -0.7008909  -2.10820389]]
@@ -125,12 +115,7 @@
 1 3.318 [[ 0.66219079  0.74796319 -1.14612854]
  [-0.81948912  0.03000021  1.68936598]
  [ 0.23214608 -0.33772916 -1.94628811]]
-2 2.0218 [[ 0.64342022  0.74127686 -1.12067163]
- [-0.81161296 -0.00900121  1.72049117]
- [ 0.2086665  -0.35079569 -1.909742  ]]
-
 ...
-
 199 0.672261 [[-1.15377033  0.28146935  1.13632679]
  [ 0.37484586  0.18958236  0.33544877]
  [-0.35609841 -0.43973011 -1.25604188]]