Add Model Creator

Author: amitshekhariitbhu

.gitignore

@@ -8,3 +8,5 @@
 /captures
 .externalNativeBuild
 /.idea
+/MNIST_data
+/model

mnist.py

@@ -0,0 +1,184 @@
+from __future__ import print_function
+import shutil
+import os.path
+import tensorflow as tf
+from tensorflow.examples.tutorials.mnist import input_data
+
+EXPORT_DIR = './model'
+
+if os.path.exists(EXPORT_DIR):
+    shutil.rmtree(EXPORT_DIR)
+
+mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
+
+# Parameters
+learning_rate = 0.001
+training_iters = 200000
+batch_size = 128
+display_step = 10
+
+# Network Parameters
+n_input = 784  # MNIST data input (img shape: 28*28)
+n_classes = 10  # MNIST total classes (0-9 digits)
+dropout = 0.75  # Dropout, probability to keep units
+
+# tf Graph input
+x = tf.placeholder(tf.float32, [None, n_input])
+y = tf.placeholder(tf.float32, [None, n_classes])
+keep_prob = tf.placeholder(tf.float32)  # dropout (keep probability)
+
+
+# Create some wrappers for simplicity
+def conv2d(x, W, b, strides=1):
+    # Conv2D wrapper, with bias and relu activation
+    x = tf.nn.conv2d(x, W, strides=[1, strides, strides, 1], padding='SAME')
+    x = tf.nn.bias_add(x, b)
+    return tf.nn.relu(x)
+
+
+def maxpool2d(x, k=2):
+    # MaxPool2D wrapper
+    return tf.nn.max_pool(x, ksize=[1, k, k, 1], strides=[1, k, k, 1],
+                          padding='SAME')
+
+
+# Create Model
+def conv_net(x, weights, biases, dropout):
+    # Reshape input picture
+    x = tf.reshape(x, shape=[-1, 28, 28, 1])
+
+    # Convolution Layer
+    conv1 = conv2d(x, weights['wc1'], biases['bc1'])
+    # Max Pooling (down-sampling)
+    conv1 = maxpool2d(conv1, k=2)
+
+    # Convolution Layer
+    conv2 = conv2d(conv1, weights['wc2'], biases['bc2'])
+    # Max Pooling (down-sampling)
+    conv2 = maxpool2d(conv2, k=2)
+
+    # Fully connected layer
+    # Reshape conv2 output to fit fully connected layer input
+    fc1 = tf.reshape(conv2, [-1, weights['wd1'].get_shape().as_list()[0]])
+    fc1 = tf.add(tf.matmul(fc1, weights['wd1']), biases['bd1'])
+    fc1 = tf.nn.relu(fc1)
+    # Apply Dropout
+    fc1 = tf.nn.dropout(fc1, dropout)
+
+    # Output, class prediction
+    out = tf.add(tf.matmul(fc1, weights['out']), biases['out'])
+    return out
+
+
+# Store layers weight & bias
+weights = {
+    # 5x5 conv, 1 input, 32 outputs
+    'wc1': tf.Variable(tf.random_normal([5, 5, 1, 32])),
+    # 5x5 conv, 32 inputs, 64 outputs
+    'wc2': tf.Variable(tf.random_normal([5, 5, 32, 64])),
+    # fully connected, 7*7*64 inputs, 1024 outputs
+    'wd1': tf.Variable(tf.random_normal([7 * 7 * 64, 1024])),
+    # 1024 inputs, 10 outputs (class prediction)
+    'out': tf.Variable(tf.random_normal([1024, n_classes]))
+}
+
+biases = {
+    'bc1': tf.Variable(tf.random_normal([32])),
+    'bc2': tf.Variable(tf.random_normal([64])),
+    'bd1': tf.Variable(tf.random_normal([1024])),
+    'out': tf.Variable(tf.random_normal([n_classes]))
+}
+
+# Construct model
+pred = conv_net(x, weights, biases, keep_prob)
+
+# Define loss and optimizer
+cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(pred, y))
+optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost)
+
+# Evaluate model
+correct_pred = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1))
+accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))
+
+# Initializing the variables
+init = tf.initialize_all_variables()
+
+# Launch the graph
+with tf.Session() as sess:
+    sess.run(init)
+    step = 1
+    # Keep training until reach max iterations
+    while step * batch_size < training_iters:
+        batch_x, batch_y = mnist.train.next_batch(batch_size)
+        # Run optimization op (backprop)
+        sess.run(optimizer, feed_dict={x: batch_x, y: batch_y,
+                                       keep_prob: dropout})
+        if step % display_step == 0:
+            # Calculate batch loss and accuracy
+            loss, acc = sess.run([cost, accuracy], feed_dict={x: batch_x,
+                                                              y: batch_y,
+                                                              keep_prob: 1.})
+            print("Iter " + str(step * batch_size) + ", Minibatch Loss= " + \
+                  "{:.6f}".format(loss) + ", Training Accuracy= " + \
+                  "{:.5f}".format(acc))
+        step += 1
+    print("Optimization Finished!")
+
+    # Calculate accuracy for 256 mnist test images
+    print("Testing Accuracy:", \
+          sess.run(accuracy, feed_dict={x: mnist.test.images[:256],
+                                        y: mnist.test.labels[:256],
+                                        keep_prob: 1.}))
+    WC1 = weights['wc1'].eval(sess)
+    BC1 = biases['bc1'].eval(sess)
+    WC2 = weights['wc2'].eval(sess)
+    BC2 = biases['bc2'].eval(sess)
+    WD1 = weights['wd1'].eval(sess)
+    BD1 = biases['bd1'].eval(sess)
+    W_OUT = weights['out'].eval(sess)
+    B_OUT = biases['out'].eval(sess)
+
+# Create new graph for exporting
+g = tf.Graph()
+with g.as_default():
+    x_2 = tf.placeholder("float", shape=[None, 784], name="input")
+
+    WC1 = tf.constant(WC1, name="WC1")
+    BC1 = tf.constant(BC1, name="BC1")
+    x_image = tf.reshape(x_2, [-1, 28, 28, 1])
+    CONV1 = conv2d(x_image, WC1, BC1)
+    MAXPOOL1 = maxpool2d(CONV1, k=2)
+
+    WC2 = tf.constant(WC2, name="WC2")
+    BC2 = tf.constant(BC2, name="BC2")
+    CONV2 = conv2d(MAXPOOL1, WC2, BC2)
+    MAXPOOL2 = maxpool2d(CONV2, k=2)
+
+    WD1 = tf.constant(WD1, name="WD1")
+    BD1 = tf.constant(BD1, name="BD1")
+
+    FC1 = tf.reshape(MAXPOOL2, [-1, WD1.get_shape().as_list()[0]])
+    FC1 = tf.add(tf.matmul(FC1, WD1), BD1)
+    FC1 = tf.nn.relu(FC1)
+
+    W_OUT = tf.constant(W_OUT, name="W_OUT")
+    B_OUT = tf.constant(B_OUT, name="B_OUT")
+
+    # skipped dropout for exported graph.
+
+    OUTPUT = tf.nn.softmax(tf.matmul(FC1, W_OUT) + B_OUT, name="output")
+
+    sess = tf.Session()
+    init = tf.initialize_all_variables()
+    sess.run(init)
+
+    graph_def = g.as_graph_def()
+    tf.train.write_graph(graph_def, EXPORT_DIR, 'mnist_model_graph.pb', as_text=False)
+
+    # Test trained model
+    y_train = tf.placeholder("float", [None, 10])
+    correct_prediction = tf.equal(tf.argmax(OUTPUT, 1), tf.argmax(y_train, 1))
+    accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))
+
+    print("check accuracy %g" % accuracy.eval(
+            {x_2: mnist.test.images, y_train: mnist.test.labels}, sess))