Added TF csv reader

data-01-test-score.csv

@@ -1,11 +1,3 @@
-# Test Scores for General Psychology
-# http://college.cengage.com/mathematics/brase/understandable_statistics/7e/students/datasets/mlr/frames/frame.html
-# The data (X1, X2, X3, X4) are for each student.
-# X1 = score on exam #1
-# X2 = score on exam #2
-# X3 = score on exam #3
-# X4 = score on final exam
-# EXAM1,EXAM2,EXAM3,FINAL
 73,80,75,152
 93,88,93,185
 89,91,90,180

lab-03-X-minimizing_cost_tf_gradient.py

@@ -25,22 +25,24 @@
 
 # Get gradients
 gvs = optimizer.compute_gradients(cost)
+# Optional: modify gradient if necessary
+# gvs = [(tf.clip_by_value(grad, -1., 1.), var) for grad, var in gvs]
 # Apply gradients
-capped_gvs = [(tf.clip_by_value(grad, -1., 1.), var) for grad, var in gvs]
-apply_gradients = optimizer.apply_gradients(capped_gvs)
+apply_gradients = optimizer.apply_gradients(gvs)
 
 # Launch the graph in a session.
 sess = tf.Session()
 # Initializes global variables in the graph.
 sess.run(tf.global_variables_initializer())
 
 for step in range(100):
-    print(step, sess.run([gradient, W, gvs, capped_gvs]))
+    print(step, sess.run([gradient, W, gvs]))
     sess.run(apply_gradients)
     # Same as sess.run(train)
 
 
 '''
+# Apply gradients
 0 [37.333332, 5.0, [(37.333336, 5.0)]]
 1 [33.848888, 4.6266665, [(33.848888, 4.6266665)]]
 2 [30.689657, 4.2881775, [(30.689657, 4.2881775)]]

lab-04-1-multi_variable_linear_regression.py

@@ -34,14 +34,12 @@
 sess = tf.Session()
 # Initializes global variables in the graph.
 sess.run(tf.global_variables_initializer())
-# Set up feed_dict variables inside the loop.
-feed = {x1: x1_data, x2: x2_data, x3: x3_data, Y: y_data}
 
 for step in range(2001):
-    sess.run(train, feed_dict=feed)
+    cost_val, hy_val, _ = sess.run([cost, hypothesis, train],
+                                   feed_dict={x1: x1_data, x2: x2_data, x3: x3_data, Y: y_data})
     if step % 10 == 0:
-        print(step, "Cost: ", sess.run(cost, feed_dict=feed),
-              "\nPrediction:\n", sess.run(hypothesis, feed_dict=feed))
+        print(step, "Cost: ", cost_val, "\nPrediction:\n", hy_val)
 
 '''
 0 Cost:  19614.8

lab-04-2-multi_variable_matmul_linear_regression.py

@@ -28,14 +28,12 @@
 sess = tf.Session()
 # Initializes global variables in the graph.
 sess.run(tf.global_variables_initializer())
-# Set up feed_dict variables inside the loop.
-feed = {X: x_data, Y: y_data}
 
 for step in range(2001):
-    sess.run(train, feed_dict=feed)
+    cost_val, hy_val, _ = sess.run(
+        [cost, hypothesis, train], feed_dict={X: x_data, Y: y_data})
     if step % 10 == 0:
-        print(step, "Cost: ", sess.run(cost, feed_dict=feed),
-              "\nPrediction:\n", sess.run(hypothesis, feed_dict=feed))
+        print(step, "Cost: ", cost_val, "\nPrediction:\n", hy_val)
 
 '''
 0 Cost:  7105.46

lab-04-3-file_input_linear_regression.py

@@ -32,14 +32,12 @@
 sess = tf.Session()
 # Initializes global variables in the graph.
 sess.run(tf.global_variables_initializer())
-# Set up feed_dict variables inside the loop.
-feed = {X: x_data, Y: y_data}
 
 for step in range(2001):
-    sess.run(train, feed_dict=feed)
+    cost_val, hy_val, _ = sess.run(
+        [cost, hypothesis, train], feed_dict={X: x_data, Y: y_data})
     if step % 10 == 0:
-        print(step, "Cost: ", sess.run(cost, feed_dict=feed),
-              "\nPrediction:\n", sess.run(hypothesis, feed_dict=feed))
+        print(step, "Cost: ", cost_val, "\nPrediction:\n", hy_val)
 
 # Ask my score
 print("Your score will be ", sess.run(

lab-04-4-tf_reader_linear_regression.py

@@ -0,0 +1,71 @@
+# Lab 4 Multi-variable linear regression
+# https://www.tensorflow.org/programmers_guide/reading_data
+
+import tensorflow as tf
+import numpy as np
+tf.set_random_seed(777)  # for reproducibility
+
+filename_queue = tf.train.string_input_producer(
+    ['data-01-test-score.csv'], shuffle=False, name='filename_queue')
+
+reader = tf.TextLineReader()
+key, value = reader.read(filename_queue)
+
+# Default values, in case of empty columns. Also specifies the type of the
+# decoded result.
+record_defaults = [[0.], [0.], [0.], [0.]]
+xy = tf.decode_csv(value, record_defaults=record_defaults)
+
+# collect batches of cvs in
+train_x_batch, train_y_batch = \
+    tf.train.batch([xy[0:-1], xy[-1:]], batch_size=10)
+
+# placeholders for a tensor that will be always fed.
+X = tf.placeholder(tf.float32, shape=[None, 3])
+Y = tf.placeholder(tf.float32, shape=[None, 1])
+
+W = tf.Variable(tf.random_normal([3, 1]), name='weight')
+b = tf.Variable(tf.random_normal([1]), name='bias')
+
+# Hypothesis
+hypothesis = tf.matmul(X, W) + b
+
+# Simplified cost/loss function
+cost = tf.reduce_mean(tf.square(hypothesis - Y))
+
+# Minimize
+optimizer = tf.train.GradientDescentOptimizer(learning_rate=1e-5)
+train = optimizer.minimize(cost)
+
+# Launch the graph in a session.
+sess = tf.Session()
+# Initializes global variables in the graph.
+sess.run(tf.global_variables_initializer())
+
+# Start populating the filename queue.
+coord = tf.train.Coordinator()
+threads = tf.train.start_queue_runners(sess=sess, coord=coord)
+
+for step in range(2001):
+    x_batch, y_batch = sess.run([train_x_batch, train_y_batch])
+    cost_val, hy_val, _ = sess.run(
+        [cost, hypothesis, train], feed_dict={X: x_batch, Y: y_batch})
+    if step % 10 == 0:
+        print(step, "Cost: ", cost_val, "\nPrediction:\n", hy_val)
+
+coord.request_stop()
+coord.join(threads)
+
+# Ask my score
+print("Your score will be ",
+      sess.run(hypothesis, feed_dict={X: [[100, 70, 101]]}))
+
+print("Other scores will be ",
+      sess.run(hypothesis, feed_dict={X: [[60, 70, 110], [90, 100, 80]]}))
+
+'''
+Your score will be  [[ 181.73277283]]
+Other scores will be  [[ 145.86265564]
+ [ 187.23129272]]
+
+'''

lab-07-2-learning_rate_and_evaluation.py

@@ -41,14 +41,15 @@
     for epoch in range(training_epochs):
         avg_cost = 0
         total_batch = int(mnist.train.num_examples / batch_size)
-        
+
         for i in range(total_batch):
             batch_xs, batch_ys = mnist.train.next_batch(batch_size)
             feed_dict = {X: batch_xs, Y: batch_ys}
             c, _ = sess.run([cost, optimizer], feed_dict=feed_dict)
             avg_cost += c / total_batch
-
-        print('Epoch:', '%04d' % (epoch + 1), 'cost =', '{:.9f}'.format(avg_cost))
+
+        print('Epoch:', '%04d' % (epoch + 1),
+              'cost =', '{:.9f}'.format(avg_cost))
 
     print("Learning finished")
 
@@ -62,7 +63,8 @@
     print("Prediction: ", sess.run(
         tf.argmax(hypothesis, 1), feed_dict={X: mnist.test.images[r:r + 1]}))
 
-    plt.imshow(mnist.test.images[r:r + 1].reshape(28, 28), cmap='Greys', interpolation='nearest')
+    plt.imshow(mnist.test.images[r:r + 1].reshape(28,
+                                                  28), cmap='Greys', interpolation='nearest')
     plt.show()
 
 

lab-09-1-xor.py

@@ -7,8 +7,8 @@
 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])
-Y = tf.placeholder(tf.float32,[None,1])
+X = tf.placeholder(tf.float32, [None, 2])
+Y = tf.placeholder(tf.float32, [None, 1])
 
 W = tf.Variable(tf.random_normal([2, 1]), name='weight')
 b = tf.Variable(tf.random_normal([1]), name='bias')

lab-09-2-xor-nn.py

@@ -7,8 +7,8 @@
 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])
-Y = tf.placeholder(tf.float32,[None,1])
+X = tf.placeholder(tf.float32, [None, 2])
+Y = tf.placeholder(tf.float32, [None, 1])
 
 W1 = tf.Variable(tf.random_normal([2, 2]), name='weight1')
 b1 = tf.Variable(tf.random_normal([2]), name='bias1')

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

@@ -7,8 +7,8 @@
 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])
-Y = tf.placeholder(tf.float32,[None,1])
+X = tf.placeholder(tf.float32, [None, 2])
+Y = tf.placeholder(tf.float32, [None, 1])
 
 W1 = tf.Variable(tf.random_normal([2, 10]), name='weight1')
 b1 = tf.Variable(tf.random_normal([10]), name='bias1')

lab-12-5-rnn_stock_prediction.py

@@ -7,7 +7,8 @@
 def MinMaxScaler(data):
     numerator = data - np.min(data, 0)
     denominator = np.max(data, 0) - np.min(data, 0)
-    return numerator / (denominator + 1e-7)  # noise term prevents the zero division
+    # noise term prevents the zero division
+    return numerator / (denominator + 1e-7)
 
 
 timesteps = seq_length = 7
@@ -33,8 +34,10 @@ def MinMaxScaler(data):
 # split to train and testing
 train_size = int(len(dataY) * 0.7)
 test_size = len(dataY) - train_size
-trainX, testX = np.array(dataX[0:train_size]), np.array(dataX[train_size:len(dataX)])
-trainY, testY = np.array(dataY[0:train_size]), np.array(dataY[train_size:len(dataY)])
+trainX, testX = np.array(dataX[0:train_size]), np.array(
+    dataX[train_size:len(dataX)])
+trainY, testY = np.array(dataY[0:train_size]), np.array(
+    dataY[train_size:len(dataY)])
 
 # input place holders
 X = tf.placeholder(tf.float32, [None, seq_length, data_dim])
@@ -65,7 +68,8 @@ def MinMaxScaler(data):
     print(i, step_loss)
 
 testPredict = sess.run(Y_pred, feed_dict={X: testX})
-print("RMSE", sess.run(rmse, feed_dict={targets: testY, predictions: testPredict}))
+print("RMSE", sess.run(rmse, feed_dict={
+      targets: testY, predictions: testPredict}))
 plt.plot(testY)
 plt.plot(testPredict)
 plt.xlabel("Time Period")