Added examples for regressions

Author: michaelmendoza

examples/regression/linear_regression.py

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+import numpy as np
+import matplotlib.pyplot as plt
+import tensorflow as tf
+
+# Create dataset
+N = 400
+data = lambda: None
+data.x = np.linspace(0, 1, N)
+data.y = np.exp(data.x) + 0.5 * np.random.rand(N)
+plt.scatter(data.x, data.y)
+
+# Training Parameters
+learning_rate = 0.001
+num_steps = 100
+
+# Setup Network
+X = tf.placeholder(tf.float32)
+Y = tf.placeholder(tf.float32)
+
+# Define Network
+w = tf.Variable(0.0, name='w')
+b = tf.Variable(0.0, name='b')
+Y_pred = X * w + b
+
+# Define loss and optimizer
+loss = tf.square(Y_pred - Y)
+optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate).minimize(loss)
+
+# Initalize varibles, and run network 
+init = tf.global_variables_initializer()
+sess = tf.Session()
+sess.run(init)
+
+# Train 
+for step in range(num_steps):
+    sess.run(optimizer, feed_dict={X:data.x, Y:data.y})
+
+w, b = sess.run([w, b])
+print(w, b)
+
+# Plot Results
+plt.plot(data.x, w * data.x + b, 'r')
+plt.show()

examples/regression/non-linear_regression.py

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+import numpy as np
+import matplotlib.pyplot as plt
+import tensorflow as tf
+
+N = 100
+
+def gen():
+    # Create dataset
+    data = lambda: None
+    data.x = np.linspace(0, 3, N)
+    data.y = np.exp(data.x) + 2 * np.random.rand(N)
+    data.x = np.reshape(data.x, (-1, 1))
+    data.y = np.reshape(data.y, (-1, 1))
+    return data
+
+# Training Parameters
+learning_rate = 0.01
+num_steps = 1000
+
+# Setup Network
+X = tf.placeholder(tf.float32, [None, 1])
+Y = tf.placeholder(tf.float32, [None, 1])
+
+# Define Network
+fc1 = tf.layers.dense(X, 100, activation=tf.nn.relu, name='fc1')
+fc2 = tf.layers.dense(fc1, 100, activation=tf.nn.relu, name='fc2')
+Y_pred = tf.layers.dense(fc2, 1, name='out')
+
+# Define loss and optimizer
+loss = tf.square(Y_pred - Y)
+optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(loss)
+
+# Initalize varibles, and run network 
+init = tf.global_variables_initializer()
+sess = tf.Session()
+sess.run(init)
+
+# Train 
+for step in range(num_steps):
+    data = gen()
+    sess.run(optimizer, feed_dict={X:data.x, Y:data.y})
+
+_x = np.reshape(np.linspace(0, 3, 10 * N), (-1,1))
+_y = sess.run(Y_pred, feed_dict={X:_x})
+
+# Reshape
+x = np.reshape(data.x, (-1)); y = np.reshape(data.y, (-1))
+_y = np.reshape(_y, (-1)); _x = np.reshape(_x, (-1))
+
+# Plot Results
+plt.scatter(x, y)
+plt.plot(_x, _y, 'r')
+plt.show()