new.txt

check_it.py

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+import tensorflow as tf
+import numpy
+import matplotlib.pyplot as plt
+rng = numpy.random
+
+# Parameters
+learning_rate = 0.01
+training_epochs = 1000
+display_step = 50
+
+# Training Data
+train_X = numpy.asarray([3.3,4.4,5.5,6.71,6.93,4.168,9.779,6.182,7.59,2.167,7.042,10.791,5.313,7.997,5.654,9.27,3.1])
+train_Y = numpy.asarray([1.7,2.76,2.09,3.19,1.694,1.573,3.366,2.596,2.53,1.221,2.827,3.465,1.65,2.904,2.42,2.94,1.3])
+n_samples = train_X.shape[0]
+
+# Graph Inputs
+X = tf.placeholder("float")
+Y = tf.placeholder("float")
+
+# Weights
+W = tf.Variable(rng.randn(),name = "Weight")
+b = tf.Variable(rng.randn(),name = "Bais")
+
+# Constructing the linear model
+# Linear Regression
+# Y = W*X + b
+pred = tf.add(tf.multiply(X,W),b)
+
+# Cost Function
+# ((Y-pred).^2)/N
+cost = tf.reduce_sum(tf.pow(pred-Y,2))/(2*n_samples)
+
+# Gradient descent optimisation
+optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost)
+
+# Assign default values
+init = tf.global_variables_initializer()
+
+# Actual Training Starts here
+with tf.Session() as sess:
+    sess.run(init)
+
+    # Use the training data to fit the model
+    for epoch in range(training_epochs):
+        # zip merges two lists of same length
+        for (x,y) in zip(train_X,train_Y):
+            # Pass elements zipped in (x,y) to the gradient optimiser
+            sess.run(optimizer,feed_dict={X:x,Y:y})
+
+        # Display the cost c and the optimised values every nth display step
+        if (epoch+1)%display_step == 0:
+            c = sess.run(cost,feed_dict={X:x,Y:y})
+            print("Epoch:", '%04d' % (epoch+1), "cost=", "{:.9f}".format(c),"W=", sess.run(W), "b=", sess.run(b))
+
+    print("Done with the optimisation")
+    training_cost = sess.run(cost, feed_dict={X:x,Y:y})
+    print("Final Cost",training_cost,"W=",sess.run(W),"b=",sess.run(b))
+
+    # Display Stuff
+    plt.plot(train_X, train_Y, 'ro', label='Original data')
+    plt.plot(train_X, sess.run(W) * train_X + sess.run(b), label='Fitted line')
+    plt.legend()
+    plt.show()#