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regressor.py
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regressor.py
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import tensorflow as tf
import numpy as np
class LogisticRegressor(object):
def __init__(self, learning_rate=1e-3, input_dim=100):
self.learning_rate = learning_rate
self.inputDim = input_dim
self.build()
self.sess = tf.InteractiveSession()
self.sess.run(tf.global_variables_initializer())
# Build the netowrk and the loss functions
def build(self):
self.x = tf.placeholder(name='x', dtype=tf.float32, shape=[None, self.inputDim])
self.W = tf.Variable(tf.zeros([self.inputDim,1]))
self.b = tf.Variable(tf.zeros([1]))
self.y = tf.placeholder(name='y', dtype=tf.float32, shape=[None,1])
self.y_hat = tf.sigmoid(tf.matmul(self.x,self.W)+self.b)
# Loss
# Reconstruction loss
# Minimize the cross-entropy loss
# H(x, x_hat) = -\Sigma x*log(x_hat) + (1-x)*log(1-x_hat)
epsilon = 1e-9
loss = -tf.reduce_sum(
self.y * tf.log(epsilon+self.y_hat) + (1-self.y) * tf.log(epsilon+ (1-self.y_hat)),
axis=1
)
self.loss = tf.reduce_mean(loss)
#self.train_op = tf.train.RMSPropOptimizer(learning_rate=self.learning_rate).minimize(self.total_loss)
self.train_op = tf.train.AdamOptimizer(
learning_rate=self.learning_rate).minimize(self.loss)
return
# Execute the forward and the backward pass
def run_single_step(self, x,y):
_, loss = self.sess.run(
[self.train_op, self.loss],
feed_dict={self.x: x,self.y:y}
)
return loss
def classifier(self, x):
y_hat = self.sess.run(self.y_hat, feed_dict={self.x: x})
labeled = [1 if item > 0.5 else 0 for item in y_hat]
return np.array(labeled)
# x -> z
def predictor(self, x):
y_hat = self.sess.run(self.y_hat, feed_dict={self.x: x})
return y_hat