42 lines
794 B
Python
42 lines
794 B
Python
import numpy as np
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import pandas as pd
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import utility
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df = utility.load_data()
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y_data = df.values[:, 0]
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x_data = df.values[:, 1:]
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m, n = x_data.shape
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import tensorflow as tf
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y = tf.Variable(y_data)
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x = tf.Variable(x_data)
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w = tf.Variable(tf.zeros((n, 1)))
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a = 0.1
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#h = tf.matmul(x, w)
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#cost = tf.reduce_mean(tf.square(h - y))
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#optimizer = tf.train.GradientDescentOptimizer(a)
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#train = optimizer.minimize(cost)
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a = tf.Variable([[1], [2], [3]])
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b = tf.Variable([1,2,3])
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with tf.Session() as sess:
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sess.run(tf.global_variables_initializer())
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#k = sess.run(a*b)
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#kk = sess.run(b*a)
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##kkk = sess.run(tf.matmul(a, b))
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#kkkk = sess.run(tf.matmul(b, a))
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h = tf.matmul(x, w)
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cost = tf.reduce_mean((h-y)**2)/2
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gradient = (h-y)*x
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values = sess.run((h, cost, gradient))
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print(values)
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