Flux tensor practice y = xw How do you initialize the vector x?
I am participating in the first practice of machine learning.
It is a monthly temperature prediction system.
train_t
has temperatures and train_x
weights for each data.
However, I have a question where is the initialization train_x
import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
from pprint import pprint
x = tf.placeholder(tf.float32,[None,5])
w = tf.Variable(tf.zeros([5,1]))
y = tf.matmul(x,w)
t = tf.placeholder(tf.float32,[None,1])
loss = tf.reduce_sum(tf.square(y-t))
train_step = tf.train.AdamOptimizer().minimize(loss)
sess = tf.Session()
sess.run(tf.initialize_all_variables())
train_t = np.array([5.2,5.7,8.6,14.9,18.2,20.4,25.5,26.4,22.8,17.5,11.1,6.6]) #montly temperature
train_t = train_t.reshape([12,1])
train_x = np.zeros([12,5])
for row, month in enumerate(range(1,13)):
for col, n in enumerate(range(0,5)):
train_x[row][col] = month**n ## why initialize like this??
i = 0
for _ in range(10000):
i += 1
sess.run(train_step,feed_dict={x:train_x,t:train_t})
if i % 1000 == 0:
loss_val = sess.run(loss,feed_dict={x:train_x,t:train_t})
print('step : %d,Loss: %f' % (i,loss_val))
w_val = sess.run(w)
pprint(w_val)
def predict(x):
result = 0.0
for n in range(0,5):
result += w_val[n][0] * x**n
return result
fig = plt.figure()
subplot = fig.add_subplot(1,1,1)
subplot.set_xlim(1,12)
subplot.scatter(range(1,13),train_t)
linex = np.linspace(1,12,100)
liney = predict(linex)
subplot.plot(linex, liney)
However I don't understand here
for row, month in enumerate(range(1,13)): #
for col, n in enumerate(range(0,5)): #
train_x[row][col] = month**n ## why initialize like this??
What does it mean? Does my book have comments on this? Why is train_x being initialized here?
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In fact, this block of code:
train_t = np.array([5.2,5.7,8.6,14.9,18.2,20.4,25.5,26.4,22.8,17.5,11.1,6.6]) #montly temperature
train_t = train_t.reshape([12,1])
train_x = np.zeros([12,5])
for row, month in enumerate(range(1,13)):
for col, n in enumerate(range(0,5)):
train_x[row][col] = month**n
Generates your data. It initializes train_t
and train_x
, which is the data to be injected into placeholders
x
andt
train_t
is a tensor of temperatures
train_x
is a tensor of a kind of weight for each temperature. They make up a dataset.
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Both train_x
and train_t
are arrays of your data training. In an array train_t
, you have the target of your model and it train_x
contains the input functions to your model.
The weight of your model (trained) is w
(which is the only one tf.Variable
in your code) that is randomly initialized.
The model you are training is a power of 4 (which is the maximum range(0, 5)
) polynomial in a linear variable month
that is in the range range(1, 13)
. This cut-off code generates functions for a polynomial of degree 4, starting with a linear variable month
.
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