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lab-04-1-multi_variable_linear_regression.py
# Lab 4 Multi-variable linear regression
import tensorflow as tf
tf.set_random_seed(777) # for reproducibility
x1_data = [73., 93., 89., 96., 73.]
x2_data = [80., 88., 91., 98., 66.]
x3_data = [75., 93., 90., 100., 70.]
y_data = [152., 185., 180., 196., 142.]
# placeholders for a tensor that will be always fed.
x1 = tf.placeholder(tf.float32)
x2 = tf.placeholder(tf.float32)
x3 = tf.placeholder(tf.float32)
Y = tf.placeholder(tf.float32)
w1 = tf.Variable(tf.random_normal([1]), name='weight1')
w2 = tf.Variable(tf.random_normal([1]), name='weight2')
w3 = tf.Variable(tf.random_normal([1]), name='weight3')
b = tf.Variable(tf.random_normal([1]), name='bias')
hypothesis = x1 * w1 + x2 * w2 + x3 * w3 + b
# cost/loss function
cost = tf.reduce_mean(tf.square(hypothesis - Y))
# Minimize. Need a very small learning rate for this data set
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())
for step in range(2001):
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: ", cost_val, "\nPrediction:\n", hy_val)
Result
'''
0 Cost: 19614.8
Prediction:
[ 21.69748688 39.10213089 31.82624626 35.14236832 32.55316544]
10 Cost: 14.0682
Prediction:
[ 145.56100464 187.94958496 178.50236511 194.86721802 146.08096313]
...
1990 Cost: 4.9197
Prediction:
[ 148.15084839 186.88632202 179.6293335 195.81796265 144.46044922]
2000 Cost: 4.89449
Prediction:
[ 148.15931702 186.8805542 179.63194275 195.81971741 144.45298767]
'''
lab-04-2-multi_variable_matmul_linear_regression.py
# Lab 4 Multi-variable linear regression
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.]]
# 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())
for step in range(2001):
cost_val, hy_val, _ = sess.run(
[cost, hypothesis, train], feed_dict={X: x_data, Y: y_data})
if step % 10 == 0:
print(step, "Cost: ", cost_val, "\nPrediction:\n", hy_val)
Result
'''
0 Cost: 7105.46
Prediction:
[[ 80.82241058]
[ 92.26364136]
[ 93.70250702]
[ 98.09217834]
[ 72.51759338]]
10 Cost: 5.89726
Prediction:
[[ 155.35159302]
[ 181.85691833]
[ 181.97254944]
[ 194.21760559]
[ 140.85707092]]
...
1990 Cost: 3.18588
Prediction:
[[ 154.36352539]
[ 182.94833374]
[ 181.85189819]
[ 194.35585022]
[ 142.03240967]]
2000 Cost: 3.1781
Prediction:
[[ 154.35881042]
[ 182.95147705]
[ 181.85035706]
[ 194.35533142]
[ 142.036026 ]]
'''
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