assignment 1

Author: chiphuyen

2017/assignments/exercises/e01.py

@@ -1,10 +1,13 @@
 """
-Simple TensorFlow exercises
+Simple exercises to get used to TensorFlow API
 You should thoroughly test your code
 """
+import os
+os.environ['TF_CPP_MIN_LOG_LEVEL']='2'
 
 import tensorflow as tf
 
+sess = tf.InteractiveSession()
 ###############################################################################
 # 1a: Create two random 0-d tensors x and y of any distribution.
 # Create a TensorFlow object that returns x + y if x > y, and x - y otherwise.
@@ -15,6 +18,7 @@
 x = tf.random_uniform([])  # Empty array as shape creates a scalar.
 y = tf.random_uniform([])
 out = tf.cond(tf.greater(x, y), lambda: tf.add(x, y), lambda: tf.subtract(x, y))
+print(sess.run(out))
 
 ###############################################################################
 # 1b: Create two 0-d tensors x and y randomly selected from the range [-1, 1).

assignments/exercises/q1.py

@@ -0,0 +1,93 @@
+"""
+Simple exercises to get used to TensorFlow API
+You should thoroughly test your code.
+TensorFlow's official documentation should be your best friend here
+CS20: "TensorFlow for Deep Learning Research"
+cs20.stanford.edu
+Created by Chip Huyen (chiphuyen@cs.stanford.edu)
+"""
+import os
+os.environ['TF_CPP_MIN_LOG_LEVEL']='2'
+
+import tensorflow as tf
+
+sess = tf.InteractiveSession()
+###############################################################################
+# 1a: Create two random 0-d tensors x and y of any distribution.
+# Create a TensorFlow object that returns x + y if x > y, and x - y otherwise.
+# Hint: look up tf.cond()
+# I do the first problem for you
+###############################################################################
+
+x = tf.random_uniform([])  # Empty array as shape creates a scalar.
+y = tf.random_uniform([])
+out = tf.cond(tf.greater(x, y), lambda: x + y, lambda: x - y)
+print(sess.run(out))
+
+###############################################################################
+# 1b: Create two 0-d tensors x and y randomly selected from the range [-1, 1).
+# Return x + y if x < y, x - y if x > y, 0 otherwise.
+# Hint: Look up tf.case().
+###############################################################################
+
+# YOUR CODE
+
+###############################################################################
+# 1c: Create the tensor x of the value [[0, -2, -1], [0, 1, 2]] 
+# and y as a tensor of zeros with the same shape as x.
+# Return a boolean tensor that yields Trues if x equals y element-wise.
+# Hint: Look up tf.equal().
+###############################################################################
+
+# YOUR CODE
+
+###############################################################################
+# 1d: Create the tensor x of value 
+# [29.05088806,  27.61298943,  31.19073486,  29.35532951,
+#  30.97266006,  26.67541885,  38.08450317,  20.74983215,
+#  34.94445419,  34.45999146,  29.06485367,  36.01657104,
+#  27.88236427,  20.56035233,  30.20379066,  29.51215172,
+#  33.71149445,  28.59134293,  36.05556488,  28.66994858].
+# Get the indices of elements in x whose values are greater than 30.
+# Hint: Use tf.where().
+# Then extract elements whose values are greater than 30.
+# Hint: Use tf.gather().
+###############################################################################
+
+# YOUR CODE
+
+###############################################################################
+# 1e: Create a diagnoal 2-d tensor of size 6 x 6 with the diagonal values of 1,
+# 2, ..., 6
+# Hint: Use tf.range() and tf.diag().
+###############################################################################
+
+# YOUR CODE
+
+###############################################################################
+# 1f: Create a random 2-d tensor of size 10 x 10 from any distribution.
+# Calculate its determinant.
+# Hint: Look at tf.matrix_determinant().
+###############################################################################
+
+# YOUR CODE
+
+###############################################################################
+# 1g: Create tensor x with value [5, 2, 3, 5, 10, 6, 2, 3, 4, 2, 1, 1, 0, 9].
+# Return the unique elements in x
+# Hint: use tf.unique(). Keep in mind that tf.unique() returns a tuple.
+###############################################################################
+
+# YOUR CODE
+
+###############################################################################
+# 1h: Create two tensors x and y of shape 300 from any normal distribution,
+# as long as they are from the same distribution.
+# Use tf.cond() to return:
+# - The mean squared error of (x - y) if the average of all elements in (x - y)
+#   is negative, or
+# - The sum of absolute value of all elements in the tensor (x - y) otherwise.
+# Hint: see the Huber loss function in the lecture slides 3.
+###############################################################################
+
+# YOUR CODE

examples/03_linreg_placeholder.py

@@ -0,0 +1,69 @@
+""" Solution for simple linear regression example using placeholders
+Created by Chip Huyen (chiphuyen@cs.stanford.edu)
+CS20: "TensorFlow for Deep Learning Research"
+cs20.stanford.edu
+Lecture 03
+"""
+import os
+os.environ['TF_CPP_MIN_LOG_LEVEL']='2'
+import time
+
+import numpy as np
+import matplotlib.pyplot as plt
+import tensorflow as tf
+
+import utils
+
+DATA_FILE = 'data/birth_life_2010.txt'
+
+# Step 1: read in data from the .txt file
+data, n_samples = utils.read_birth_life_data(DATA_FILE)
+
+# Step 2: create placeholders for X (birth rate) and Y (life expectancy)
+X = tf.placeholder(tf.float32, name='X')
+Y = tf.placeholder(tf.float32, name='Y')
+
+# Step 3: create weight and bias, initialized to 0
+w = tf.get_variable('weights', initializer=tf.constant(0.0))
+b = tf.get_variable('bias', initializer=tf.constant(0.0))
+
+# Step 4: build model to predict Y
+Y_predicted = w * X + b 
+
+# Step 5: use the squared error as the loss function
+# you can use either mean squared error or Huber loss
+loss = tf.square(Y - Y_predicted, name='loss')
+# loss = utils.huber_loss(Y, Y_predicted)
+
+# Step 6: using gradient descent with learning rate of 0.001 to minimize loss
+optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.001).minimize(loss)
+
+
+start = time.time()
+writer = tf.summary.FileWriter('./graphs/linear_reg', tf.get_default_graph())
+with tf.Session() as sess:
+	# Step 7: initialize the necessary variables, in this case, w and b
+	sess.run(tf.global_variables_initializer()) 
+	
+	# Step 8: train the model for 100 epochs
+	for i in range(100): 
+		total_loss = 0
+		for x, y in data:
+			# Session execute optimizer and fetch values of loss
+			_, l = sess.run([optimizer, loss], feed_dict={X: x, Y:y}) 
+			total_loss += l
+		print('Epoch {0}: {1}'.format(i, total_loss/n_samples))
+
+	# close the writer when you're done using it
+	writer.close() 
+	
+	# Step 9: output the values of w and b
+	w_out, b_out = sess.run([w, b]) 
+
+print('Took: %f seconds' %(time.time() - start))
+
+# plot the results
+plt.plot(data[:,0], data[:,1], 'bo', label='Real data')
+plt.plot(data[:,0], data[:,0] * w_out + b_out, 'r', label='Predicted data')
+plt.legend()
+plt.show()