Update notebooks

00.Getting Started/08.hyperdrive-with-TensorFlow/tf_mnist_score.py

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+from __future__ import print_function
+import tensorflow as tf
+import numpy as np
+import os
+import json
+import base64
+from io import BytesIO
+from PIL import Image
+
+##############################################
+# helper functions
+##############################################
+
+
+def build_model(x, y_, keep_prob):
+    def weight_variable(shape):
+        initial = tf.truncated_normal(shape, stddev=0.1)
+        return tf.Variable(initial)
+
+    def bias_variable(shape):
+        initial = tf.constant(0.1, shape=shape)
+        return tf.Variable(initial)
+
+    def conv2d(x, W):
+        return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME')
+
+    def max_pool_2x2(x):
+        return tf.nn.max_pool(x, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')
+
+    W_conv1 = weight_variable([5, 5, 1, 32])
+    b_conv1 = bias_variable([32])
+
+    x_image = tf.reshape(x, [-1, 28, 28, 1])
+
+    h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1)
+    h_pool1 = max_pool_2x2(h_conv1)
+
+    W_conv2 = weight_variable([5, 5, 32, 64])
+    b_conv2 = bias_variable([64])
+
+    h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2)
+    h_pool2 = max_pool_2x2(h_conv2)
+
+    W_fc1 = weight_variable([7 * 7 * 64, 1024])
+    b_fc1 = bias_variable([1024])
+
+    h_pool2_flat = tf.reshape(h_pool2, [-1, 7 * 7 * 64])
+    h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1)
+
+    h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)
+
+    W_fc2 = weight_variable([1024, 10])
+    b_fc2 = bias_variable([10])
+
+    y_conv = tf.matmul(h_fc1_drop, W_fc2) + b_fc2
+
+    return y_conv
+
+
+def base64ToImg(base64ImgString):
+    if base64ImgString.startswith('b\''):
+        base64ImgString = base64ImgString[2:-1]
+    base64Img = base64ImgString.encode('utf-8')
+    decoded_img = base64.b64decode(base64Img)
+    img_buffer = BytesIO(decoded_img)
+    img = Image.open(img_buffer)
+    return img
+
+##############################################
+# API init() and run() methods
+##############################################
+
+
+def init():
+    global x, keep_prob, y_conv, sess
+    g = tf.Graph()
+    with g.as_default():
+        x = tf.placeholder(tf.float32, shape=[None, 784])
+        y_ = tf.placeholder(tf.float32, shape=[None, 10])
+        keep_prob = tf.placeholder(tf.float32)
+        y_conv = build_model(x, y_, keep_prob)
+
+        saver = tf.train.Saver()
+        init_op = tf.global_variables_initializer()
+
+    model_dir = os.path.join('sample_projects', 'outputs')
+    saved_model_path = os.path.join(model_dir, 'model.ckpt')
+
+    sess = tf.Session(graph=g)
+    sess.run(init_op)
+    saver.restore(sess, saved_model_path)
+
+
+def run(input_data):
+    img = base64ToImg(json.loads(input_data)['data'])
+    img_data = np.array(img, dtype=np.float32).flatten()
+    img_data.resize((1, 784))
+
+    y_pred = sess.run(y_conv, feed_dict={x: img_data, keep_prob: 1.0})
+    predicted_label = np.argmax(y_pred[0])
+
+    outJsonString = json.dumps({"label": str(predicted_label)})
+    return str(outJsonString)

00.Getting Started/08.hyperdrive-with-TensorFlow/tf_mnist_train.py

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+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import tensorflow as tf
+# Load MNIST Data
+from tensorflow.examples.tutorials.mnist import input_data
+import os
+import argparse
+
+from azureml.core.run import Run
+
+# the following 10 lines can be removed once BUG# 241943 is fixed
+
+
+def get_logger():
+    try:
+        return Run.get_submitted_run()
+    except Exception:
+        return LocalLogger()
+
+
+class LocalLogger:
+    def log(self, key, value):
+        print("AML-Log:", key, value)
+
+
+def build_model(x, y_, keep_prob):
+    def weight_variable(shape):
+        initial = tf.truncated_normal(shape, stddev=0.1)
+        return tf.Variable(initial)
+
+    def bias_variable(shape):
+        initial = tf.constant(0.1, shape=shape)
+        return tf.Variable(initial)
+
+    def conv2d(x, W):
+        return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME')
+
+    def max_pool_2x2(x):
+        return tf.nn.max_pool(x, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')
+
+    W_conv1 = weight_variable([5, 5, 1, 32])
+    b_conv1 = bias_variable([32])
+
+    x_image = tf.reshape(x, [-1, 28, 28, 1])
+
+    h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1)
+    h_pool1 = max_pool_2x2(h_conv1)
+
+    W_conv2 = weight_variable([5, 5, 32, 64])
+    b_conv2 = bias_variable([64])
+
+    h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2)
+    h_pool2 = max_pool_2x2(h_conv2)
+
+    W_fc1 = weight_variable([7 * 7 * 64, 1024])
+    b_fc1 = bias_variable([1024])
+
+    h_pool2_flat = tf.reshape(h_pool2, [-1, 7 * 7 * 64])
+    h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1)
+
+    h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)
+
+    W_fc2 = weight_variable([1024, 10])
+    b_fc2 = bias_variable([10])
+
+    y_conv = tf.matmul(h_fc1_drop, W_fc2) + b_fc2
+
+    return y_conv
+
+
+def main():
+    # Get command-line arguments
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--learning_rate', type=float,
+                        default=0.0001, help='learning rate')
+    parser.add_argument('--minibatch_size', type=int,
+                        default=50, help='minibatch size')
+    parser.add_argument('--keep_probability', type=float,
+                        default=0.5, help='keep probability for dropout layer')
+    parser.add_argument('--num_iterations', type=int,
+                        default=1000, help='number of iterations')
+    parser.add_argument('--output_dir', type=str, default='./outputs',
+                        help='output directory to write checkpoints to')
+
+    args = parser.parse_args()
+
+    # log parameters
+    run_logger = get_logger()
+    run_logger.log("learning_rate", args.learning_rate)
+    run_logger.log("minibatch_size", args.minibatch_size)
+    run_logger.log("keep_probability", args.keep_probability)
+    run_logger.log("num_iterations", args.num_iterations)
+
+    # Load MNIST data
+    mnist = input_data.read_data_sets('MNIST_data', one_hot=True)
+
+    sess = tf.InteractiveSession()
+
+    x = tf.placeholder(tf.float32, shape=[None, 784])
+    y_ = tf.placeholder(tf.float32, shape=[None, 10])
+    keep_prob = tf.placeholder(tf.float32)
+
+    y_conv = build_model(x, y_, keep_prob)
+
+    cross_entropy = tf.reduce_mean(
+        tf.nn.softmax_cross_entropy_with_logits(labels=y_, logits=y_conv))
+
+    train_step = tf.train.AdamOptimizer(
+        args.learning_rate).minimize(cross_entropy)
+    correct_prediction = tf.equal(tf.argmax(y_conv, 1), tf.argmax(y_, 1))
+
+    accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
+    sess.run(tf.global_variables_initializer())
+
+    for i in range(args.num_iterations):
+        batch = mnist.train.next_batch(args.minibatch_size)
+        if i % 100 == 0:
+            test_acc = accuracy.eval(
+                feed_dict={x: mnist.test.images, y_: mnist.test.labels, keep_prob: 1.0})
+            train_accuracy = accuracy.eval(
+                feed_dict={x: batch[0], y_: batch[1], keep_prob: 1.0})
+            print("step %d, training accuracy %g, test accuracy, %g" %
+                  (i, train_accuracy, test_acc))
+
+            # log test accuracy to AML
+            run_logger.log("Accuracy", float(test_acc))
+            run_logger.log("Iterations", i)
+
+        sess.run(train_step, feed_dict={
+                 x: batch[0], y_: batch[1], keep_prob: args.keep_probability})
+
+    # Save the trained model
+    model_dir = args.output_dir
+    model_file = 'model.ckpt'
+
+    os.makedirs(model_dir, exist_ok=True)
+
+    saver = tf.train.Saver()
+    saver.save(sess, os.path.join(model_dir, model_file))
+
+    final_test_acc = sess.run(accuracy, feed_dict={
+                              x: mnist.test.images, y_: mnist.test.labels, keep_prob: 1.0})
+    run_logger.log("Accuracy", float(final_test_acc))
+    run_logger.log("Iterations", args.num_iterations)
+    print("test accuracy %g" % final_test_acc)
+
+
+if __name__ == "__main__":
+    main()