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Below is an instruction that describes a task. Write a response that appropriately completes the request. ### Instruction: Generate a neural network model with TensorFlow and Python for classifying hand-written digits from the MNIST dataset. ### Input: Not applicable ### Output: import tensorflow as tf # Import MNIST data from tensorflow.examples.tutorials.mnist import input_data mnist = input_data.read_data_sets("/tmp/data", one_hot=True) # Parameters learning_rate = 0.001 training_epochs = 15 batch_size = 100 display_step = 1 # Network Parameters n_hidden_1 = 256 # 1st layer number of neurons n_hidden_2 = 256 # 2nd layer number of neurons n_input = 784 # MNIST data input (img shape: 28*28) n_classes = 10 # MNIST total classes (0-9 digits) # tf Graph input X = tf.placeholder("float", [None, n_input]) Y = tf.placeholder("float", [None, n_classes]) # Store layers weight & bias weights = { 'h1': tf.Variable(tf.random_normal([n_input, n_hidden_1])), 'h2': tf.Variable(tf.random_normal([n_hidden_1, n_hidden_2])), 'out': tf.Variable(tf.random_normal([n_hidden_2, n_classes])) } biases = { 'b1': tf.Variable(tf.random_normal([n_hidden_1])), 'b2': tf.Variable(tf.random_normal([n_hidden_2])), 'out': tf.Variable(tf.random_normal([n_classes])) } # Create model def multilayer_perceptron(x): # Hidden fully connected layer with 256 neurons layer_1 = tf.add(tf.matmul(x, weights['h1']), biases['b1']) # Hidden fully connected layer with 256 neurons layer_2 = tf.add(tf.matmul(layer_1, weights['h2']), biases['b2']) # Output fully connected layer with a neuron for each class out_layer = tf.matmul(layer_2, weights['out']) + biases['out'] return out_layer # Construct model logits = multilayer_perceptron(X) # Define loss and optimizer loss_op = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits( logits=logits, labels=Y)) optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate) train_op = optimizer.minimize(loss_op) # Initializing the variables init = tf.global_variables_initializer() # Train model model = tf.Session() model.run(init) # Training cycle for epoch in range(training_epochs): avg_cost = 0. total_batch = int(mnist.train.num_examples/batch_size) # Loop over all batches for i in range(total_batch): batch_x, batch_y = mnist.train.next_batch(batch_size) # Run optimization op (backprop) and cost op (to get loss value) _, c = model.run([train_op, loss_op], feed_dict={X: batch_x, Y: batch_y}) # Compute average loss avg_cost += c / total_batch # Display logs per epoch step if epoch % display_step == 0: print("Epoch:", '%04d' % (epoch+1), "cost=", \ "{:.9f}".format(avg_cost)) print("Optimization Finished!") # Test model pred = tf.nn.softmax(logits) # Apply softmax to logits correct_prediction = tf.equal(tf.argmax(pred, 1), tf.argmax(Y, 1)) # Calculate accuracy accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float")) print("Accuracy:", accuracy.eval({X: mnist.test.images, Y: mnist.test.labels}, model))

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