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import pandas as pd #work with data as tables
import numpy as np #use number matrices
import matplotlib.pyplot as plt
import tensorflow as tf
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# step 1
dataframe = pd.read_csv('data.csv')
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dataframe.head()
dataframe = dataframe[0:10]
dataframe
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dataframe = dataframe.drop(['index', 'price','sq_price'], axis=1)
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dataframe.head()
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# step 2
# 1 is good buy and 0 is bad buy
dataframe.loc[:, ('y1')] = [1, 1, 1, 0, 0, 1, 0, 1, 1,1]
dataframe.loc[:, ('y2')] = dataframe['y1'] == 0
dataframe.loc[:, ('y2')] = dataframe['y2'].astype(int)
dataframe
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# step 3 - prepare data for tensorflow (tensors)
# tensors are a generic version of vectors and matrices
#vector is a list of list of numbers (1D tensor)
#matrix is a list of list numbers (2D Tensor)
#list of list of list of numbers (3D tensor)
#......
#convert features to input tensor
inputX = dataframe.loc[:, ['area', 'bathrooms']].as_matrix()
#convert labels to input tensors
inputY = dataframe.loc[:, ['y1', 'y2']].as_matrix()
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#step 4 - write out our hyperparneters
learning_rate = 0.000001
training_epochs = 2000
display_step = 50
n_samples = inputY.size
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#step 5 - Create our computation graph/neural network
x = tf.placeholder(tf.float32, [None, 2])
W = tf.Variable(tf.zeros([2, 2]))
b = tf.Variable(tf.zeros([2]))
y_values = tf.add(tf.matmul(x, W), b)
y = tf.nn.softmax(y_values)
y_ = tf.placeholder(tf.float32, [None,2])
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#step 6 - perform training
# Cost function: Mean squared error
cost = tf.reduce_sum(tf.pow(y_ - y, 2))/(2*n_samples)
# Gradient descent
optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost)
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# Initialize variabls and tensorflow session
init = tf.initialize_all_variables()
sess = tf.Session()
sess.run(init)
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for i in range(training_epochs):
sess.run(optimizer, feed_dict={x: inputX, y_: inputY}) # Take a gradient descent step using our inputs and labels
# That's all! The rest of the cell just outputs debug messages.
# Display logs per epoch step
if (i) % display_step == 0:
cc = sess.run(cost, feed_dict={x: inputX, y_:inputY})
print("Training step:", '%04d' % (i), "cost=", "{:.9f}".format(cc)) #, \"W=", sess.run(W), "b=", sess.run(b)
print("Optimization Finished!")
training_cost = sess.run(cost, feed_dict={x: inputX, y_: inputY})
print("Training cost=", training_cost, "W=", sess.run(W), "b=", sess.run(b), '\n')
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sess.run(y, feed_dict={x: inputX })
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