In [1]:
import tensorflow as tf
import numpy as np
rng = np.random
import matplotlib.pyplot as plt
learning_rate = 0.0001
training_epochs = 1000
display_step = 50
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with tf.name_scope("Creation_of_array"):
x_array=np.asarray([1.0,7.7,5.32,7.88,-4.23,0.11,6.57,-1.25,-3.31,9.45])
y_array=np.asarray([1.77,2.24,-1.08,3.25,7.41,4.09,-3.66,-22.77,0.001,2.25])
x = tf.constant(x_array,dtype = tf.float32,name = "x_array")
y = tf.constant(y_array,dtype = tf.float32, name= "y_array")
with tf.name_scope("Calculating_y_mean"):
mean_y = tf.reduce_mean(y, name = "mean_y")
with tf.Session() as sess:
result_y = sess.run(mean_y)
print(result_y)
In [3]:
with tf.name_scope("Calculating_x_mean_and_x_variance"):
mean_x, variance = tf.nn.moments(x, [0], name = "mean_x_and_variance_x")
with tf.Session() as sess:
m, v = sess.run([mean_x, variance])
print(m)
print(v)
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with tf.name_scope("Calculating_covariance"):
def tensorflow_covariance(x_array,y_array,x_mean,y_mean):
cov = 0.0
for i in range(0,10):
x_val = tf.subtract(x_array[i],x_mean, name="Finding_difference_of_xval_and_mean")
y_val = tf.subtract(y_array[i],y_mean, name="Finding_difference_of_yval_and_mean")
total_val = tf.multiply(x_val,y_val, name="Multiplying_found_values")
cov = tf.add(cov,total_val, name="Recursive_addition")
return cov/10.0
with tf.Session() as sess:
covar = sess.run(tensorflow_covariance(x,y,m,result_y))
print(covar)
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with tf.name_scope("Calculating_slope_m_and_c"):
slope = tf.div(covar,v,name="Finding_slope")
intm = tf.multiply(slope,m,name = "Intermediate_step")
c_intm = tf.subtract(result_y,intm,name = "Finding_c")
with tf.Session() as sess:
m_slope = sess.run(slope)
c = sess.run(c_intm)
print(m_slope)
print(c)
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###Part-2: Plotting graph for actual values against predicted values
In [7]:
with tf.name_scope("Plotting"):
n_samples = x_array.shape[0]
X = tf.placeholder("float")
Y = tf.placeholder("float")
# Set model weights
W = tf.Variable(rng.randn(), name="weight")
b = tf.Variable(rng.randn(), name="bias")
# Construct a linear model
pred = tf.add(tf.multiply(X, W), b)
# Mean squared error
cost = tf.reduce_sum(tf.pow(pred-Y, 2))/(2*n_samples)
# Gradient descent
optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost)
# Initializing the variables
init = tf.global_variables_initializer()
# Launch the graph
with tf.Session() as sess:
sess.run(init)
# Fit all training data
for epoch in range(training_epochs):
for (p, r) in zip(x_array, y_array):
sess.run(optimizer, feed_dict={X: p, Y: r})
# Display logs per epoch step
if (epoch+1) % display_step == 0:
c = sess.run(cost, feed_dict={X: x_array, Y:y_array})
print("Epoch:", '%04d' % (epoch+1), "cost=", "{:.9f}".format(c), \
"W=", sess.run(W), "b=", sess.run(b))
print("Optimization Finished!")
training_cost = sess.run(cost, feed_dict={X: x_array, Y: y_array})
print("Training cost=", training_cost, "W=", sess.run(W), "b=", sess.run(b), '\n')
# Graphic display
plt.plot(x_array, y_array, 'ro', label='Original data')
plt.plot(x_array, sess.run(W) * x_array + sess.run(b), label='Fitted line')
plt.legend()
plt.show()
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###root mean square error
with tf.name_scope("Finding_root_mean_square_error"):
rms = tf.sqrt(tf.reduce_mean(tf.squared_difference(x_array, y_array,name = "Finding_squared_difference"),name="Finding_mean"),name = "Finding_square_root")
with tf.Session() as sess:
rmse=sess.run(rms)
print(rmse)
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with tf.name_scope("Finding_theta_1"):
y_var = tf.subtract(y,result_y,name = "Subtract_y_array_with_y_mean")
x_var = tf.subtract(x,m,name = "Subtract_x_array_with_x_mean")
mult = tf.multiply(x_var,y_var,name = "Multiply_calculated_arrays")
sumn = tf.reduce_sum(mult,name = "Find_sum_of_x_i_minus_mean_x_and_y_i_minus_mean_y")
x_var2 = tf.multiply(x_var,x_var,name = "Squaring_found_arrray_values")
sumd = tf.reduce_sum(x_var2,name = "Find_sum_of_array_of_x_i_minus_mean_x")
val = sumn/sumd
with tf.Session() as sess:
res = sess.run(val)
print(res)
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with tf.name_scope("Finding_theta_0"):
temp = tf.multiply(res,m,name = "Multiply_res_with_slope")
theta = tf.subtract(result_y,temp,name="Sub_obtained_res_with_mean_y")
with tf.Session() as sess:
theta0 = sess.run(theta)
print(theta0)
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with tf.name_scope("Finding_predictions"):
mx = tf.multiply(res,x,name = "Multiply_res_with_x_array")
y_temp = tf.add(mx,theta0,name = "Add_m_multiplied_x_array_with_c")
with tf.Session() as sess:
y_new = sess.run(y_temp)
print(y_new)
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t_minus = tf.subtract(y_new,y,name = "Sub_new_preds_with_original_y")
t_squared = tf.multiply(t_minus,t_minus,name= "Square_obtained_res")
t_sum = tf.reduce_sum(t_squared,name="Find_array_sum")
j_theta = tf.div(t_sum,20,name="Divide_by_no_of_elements")
with tf.Session() as sess:
print(sess.run(j_theta))
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with tf.Session() as sess:
writer = tf.summary.FileWriter("/tmp/tboard/output_regg2", sess.graph)
print(sess.run(j_theta))
writer.close()
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