Logistic regression model trainning.py

# Computes softmax cross entropy between logits and labels
# Measures the probability error in discrete classification tasks
# For example, each font image is labeled with one and only one label: an image can be font SansSerif or Serif, but not both.
cross_entropy = tf.reduce_mean(
        tf.nn.softmax_cross_entropy_with_logits_v2(
        logits = y + 1e-50, labels = y_))

# GradientDescentOptimizer is used to minimize loss
train_step = tf.train.GradientDescentOptimizer(
                0.02).minimize(cross_entropy)

# Define accuracy
correct_prediction = tf.equal(tf.argmax(y,1),
                     tf.argmax(y_,1))
accuracy = tf.reduce_mean(tf.cast(
           correct_prediction, "float"))


# Train for 3000 times
epochs = 3000
train_acc = np.zeros(epochs//10)
test_acc = np.zeros(epochs//10)
for i in tqdm(range(epochs)):
    # Record the accuracy
    if i % 10 == 0:
        # Check accuracy on train set
        A = accuracy.eval(feed_dict={
            x: train_dataset,
            y_: train_labels})
        train_acc[i//10] = A
        # And now the test set
        A = accuracy.eval(feed_dict={
            x: test_dataset,
            y_: test_labels})
        test_acc[i//10] = A

    train_step.run(feed_dict={
        x: train_dataset,
        y_: train_labels})