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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:
 Intermediate Neural Network in TensorFlow
In this notebook, we convert our intermediate-depth MNIST-classifying neural network from Keras to TensorFlow (compare them side by side) following Aymeric Damien's Multi-Layer Perceptron Notebook style.
Load dependencies
Step1:  Load data
Step2:  Set neural network hyperparameters (tidier at top of file!)
Step3:  Set number of neurons for each layer
Step4:  Define placeholders Tensors for inputs and labels
Step5:  Define types of layers
Step6:  Define dictionaries for storing weights and biases for each layer -- and initialize
Step7:  Design neural network architecture
Step8:  Build model
Step9:  Define model's loss and its optimizer
Step10:  Define evaluation metrics
Step11:  Create op for variable initialization
Step12:  Train the network in a session
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Python Code:
import numpy as np
np.random.seed(42)
import tensorflow as tf
tf.set_random_seed(42)
Explanation: Intermediate Neural Network in TensorFlow
In this notebook, we convert our intermediate-depth MNIST-classifying neural network from Keras to TensorFlow (compare them side by side) following Aymeric Damien's Multi-Layer Perceptron Notebook style.
Load dependencies
End of explanation
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
Explanation: Load data
End of explanation
lr = 0.1
epochs = 10
batch_size = 128
weight_initializer = tf.contrib.layers.xavier_initializer()
Explanation: Set neural network hyperparameters (tidier at top of file!)
End of explanation
n_input = 784
n_dense_1 = 64
n_dense_2 = 64
n_classes = 10
Explanation: Set number of neurons for each layer
End of explanation
x = tf.placeholder(tf.float32, [None, n_input])
y = tf.placeholder(tf.float32, [None, n_classes])
Explanation: Define placeholders Tensors for inputs and labels
End of explanation
# dense layer with ReLU activation:
def dense(x, W, b):
    z = tf.add(tf.matmul(x, W), b)
    a = tf.nn.relu(z)
    return a
Explanation: Define types of layers
End of explanation
bias_dict = {
    'b1': tf.Variable(tf.zeros([n_dense_1])),
    'b2': tf.Variable(tf.zeros([n_dense_2])),
    'b_out': tf.Variable(tf.zeros([n_classes]))
}
weight_dict = {
    'W1': tf.get_variable('W1', [n_input, n_dense_1], initializer=weight_initializer),
    'W2': tf.get_variable('W2', [n_dense_1, n_dense_2], initializer=weight_initializer),
    'W_out': tf.get_variable('W_out', [n_dense_2, n_classes], initializer=weight_initializer),
}
Explanation: Define dictionaries for storing weights and biases for each layer -- and initialize
End of explanation
def network(x, weights, biases):
    
    # two dense hidden layers:
    dense_1 = dense(x, weights['W1'], biases['b1'])
    dense_2 = dense(dense_1, weights['W2'], biases['b2'])
    
    # linear output layer (softmax):
    out_layer_z = tf.add(tf.matmul(dense_2, weights['W_out']), biases['b_out'])
    
    return out_layer_z
Explanation: Design neural network architecture
End of explanation
predictions = network(x, weights=weight_dict, biases=bias_dict)
Explanation: Build model
End of explanation
cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=predictions, labels=y))
optimizer = tf.train.GradientDescentOptimizer(learning_rate=lr).minimize(cost)
Explanation: Define model's loss and its optimizer
End of explanation
# calculate accuracy by identifying test cases where the model's highest-probability class matches the true y label: 
correct_prediction = tf.equal(tf.argmax(predictions, 1), tf.argmax(y, 1))
accuracy_pct = tf.reduce_mean(tf.cast(correct_prediction, tf.float32)) * 100
Explanation: Define evaluation metrics
End of explanation
initializer_op = tf.global_variables_initializer()
Explanation: Create op for variable initialization
End of explanation
with tf.Session() as session:
    session.run(initializer_op)
    
    print("Training for", epochs, "epochs.")
    
    # loop over epochs: 
    for epoch in range(epochs):
        
        avg_cost = 0.0 # track cost to monitor performance during training
        avg_accuracy_pct = 0.0
        
        # loop over all batches of the epoch:
        n_batches = int(mnist.train.num_examples / batch_size)
        for i in range(n_batches):
            
            batch_x, batch_y = mnist.train.next_batch(batch_size)
            
            # feed batch data to run optimization and fetching cost and accuracy: 
            _, batch_cost, batch_acc = session.run([optimizer, cost, accuracy_pct], feed_dict={x: batch_x, y: batch_y})
            
            # accumulate mean loss and accuracy over epoch: 
            avg_cost += batch_cost / n_batches
            avg_accuracy_pct += batch_acc / n_batches
            
        # output logs at end of each epoch of training:
        print("Epoch ", '%03d' % (epoch+1), 
              ": cost = ", '{:.3f}'.format(avg_cost), 
              ", accuracy = ", '{:.2f}'.format(avg_accuracy_pct), "%", 
              sep='')
    
    print("Training Complete. Testing Model.\n")
    
    test_cost = cost.eval({x: mnist.test.images, y: mnist.test.labels})
    test_accuracy_pct = accuracy_pct.eval({x: mnist.test.images, y: mnist.test.labels})
    
    print("Test Cost:", '{:.3f}'.format(test_cost))
    print("Test Accuracy: ", '{:.2f}'.format(test_accuracy_pct), "%", sep='')
Explanation: Train the network in a session
End of explanation
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