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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
 Ch 04
Step1:  Generated some initial 2D data
Step2:  Define the labels and shuffle the data
Step3:  We'll get back to this later, but the following are test inputs that we'll use to evaluate the model
Step4:  Again, define the placeholders, variables, model, and cost function
Step5:  Train the softmax classification model
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Python Code:
%matplotlib inline
import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
Explanation: Ch 04: Concept 04
Softmax classification
Import the usual libraries:
End of explanation
learning_rate = 0.01
training_epochs = 1000
num_labels = 3
batch_size = 100
x1_label0 = np.random.normal(1, 1, (100, 1))
x2_label0 = np.random.normal(1, 1, (100, 1))
x1_label1 = np.random.normal(5, 1, (100, 1))
x2_label1 = np.random.normal(4, 1, (100, 1))
x1_label2 = np.random.normal(8, 1, (100, 1))
x2_label2 = np.random.normal(0, 1, (100, 1))
plt.scatter(x1_label0, x2_label0, c='r', marker='o', s=60)
plt.scatter(x1_label1, x2_label1, c='g', marker='x', s=60)
plt.scatter(x1_label2, x2_label2, c='b', marker='_', s=60)
plt.show()
Explanation: Generated some initial 2D data:
End of explanation
xs_label0 = np.hstack((x1_label0, x2_label0))
xs_label1 = np.hstack((x1_label1, x2_label1))
xs_label2 = np.hstack((x1_label2, x2_label2))
xs = np.vstack((xs_label0, xs_label1, xs_label2))
labels = np.matrix([[1., 0., 0.]] * len(x1_label0) + [[0., 1., 0.]] * len(x1_label1) + [[0., 0., 1.]] * len(x1_label2))
arr = np.arange(xs.shape[0])
np.random.shuffle(arr)
xs = xs[arr, :]
labels = labels[arr, :]
Explanation: Define the labels and shuffle the data:
End of explanation
test_x1_label0 = np.random.normal(1, 1, (10, 1))
test_x2_label0 = np.random.normal(1, 1, (10, 1))
test_x1_label1 = np.random.normal(5, 1, (10, 1))
test_x2_label1 = np.random.normal(4, 1, (10, 1))
test_x1_label2 = np.random.normal(8, 1, (10, 1))
test_x2_label2 = np.random.normal(0, 1, (10, 1))
test_xs_label0 = np.hstack((test_x1_label0, test_x2_label0))
test_xs_label1 = np.hstack((test_x1_label1, test_x2_label1))
test_xs_label2 = np.hstack((test_x1_label2, test_x2_label2))
test_xs = np.vstack((test_xs_label0, test_xs_label1, test_xs_label2))
test_labels = np.matrix([[1., 0., 0.]] * 10 + [[0., 1., 0.]] * 10 + [[0., 0., 1.]] * 10)
Explanation: We'll get back to this later, but the following are test inputs that we'll use to evaluate the model:
End of explanation
train_size, num_features = xs.shape
X = tf.placeholder("float", shape=[None, num_features])
Y = tf.placeholder("float", shape=[None, num_labels])
W = tf.Variable(tf.zeros([num_features, num_labels]))
b = tf.Variable(tf.zeros([num_labels]))
y_model = tf.nn.softmax(tf.matmul(X, W) + b)
cost = -tf.reduce_sum(Y * tf.log(y_model))
train_op = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost)
correct_prediction = tf.equal(tf.argmax(y_model, 1), tf.argmax(Y, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))
Explanation: Again, define the placeholders, variables, model, and cost function:
End of explanation
with tf.Session() as sess:
    tf.global_variables_initializer().run()
    for step in range(training_epochs * train_size // batch_size):
        offset = (step * batch_size) % train_size
        batch_xs = xs[offset:(offset + batch_size), :]
        batch_labels = labels[offset:(offset + batch_size)]
        err, _ = sess.run([cost, train_op], feed_dict={X: batch_xs, Y: batch_labels})
        if step % 100 == 0:
            print (step, err)
    W_val = sess.run(W)
    print('w', W_val)
    b_val = sess.run(b)
    print('b', b_val)
    print("accuracy", accuracy.eval(feed_dict={X: test_xs, Y: test_labels}))
Explanation: Train the softmax classification model:
End of explanation
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