add trainer python scripts

Author: miyosuda

trainer-script/beginner.py

@@ -0,0 +1,76 @@
+import tensorflow as tf
+import shutil
+import os.path
+
+if os.path.exists("./tmp/beginner-export"):
+    shutil.rmtree("./tmp/beginner-export")
+
+# Import data
+from tensorflow.examples.tutorials.mnist import input_data
+
+mnist = input_data.read_data_sets("./tmp/data/", one_hot=True)
+
+g = tf.Graph()
+
+with g.as_default():
+    c = tf.constant(5.0)
+    assert c.graph is g
+
+    # Create the model
+    x = tf.placeholder("float", [None, 784])
+    W = tf.Variable(tf.zeros([784, 10]), name="vaiable_W")
+    b = tf.Variable(tf.zeros([10]), name="variable_b")
+    y = tf.nn.softmax(tf.matmul(x, W) + b)
+
+    # Define loss and optimizer
+    y_ = tf.placeholder("float", [None, 10])
+    cross_entropy = -tf.reduce_sum(y_ * tf.log(y))
+    train_step = tf.train.GradientDescentOptimizer(0.01).minimize(cross_entropy)
+
+    sess = tf.Session()
+
+    # Train
+    init = tf.initialize_all_variables()
+    sess.run(init)
+
+    for i in range(1000):
+        batch_xs, batch_ys = mnist.train.next_batch(100)
+        train_step.run({x: batch_xs, y_: batch_ys}, sess)
+
+    # Test trained model
+    correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))
+    accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))
+
+    print(accuracy.eval({x: mnist.test.images, y_: mnist.test.labels}, sess))
+
+# Store variable
+_W = W.eval(sess)
+_b = b.eval(sess)
+
+sess.close()
+
+# Create new graph for exporting
+g2 = tf.Graph()
+with g2.as_default():
+    # Reconstruct graph
+    x2 = tf.placeholder("float", [None, 784], name="input")
+    W2 = tf.constant(_W, name="constant_W")
+    b2 = tf.constant(_b, name="constant_b")
+    y2 = tf.nn.softmax(tf.matmul(x2, W2) + b2, name="output")
+
+    sess2 = tf.Session()
+
+    init2 = tf.initialize_all_variables();
+    sess2.run(init2)
+
+    
+    graph_def = g2.as_graph_def()
+    
+    tf.train.write_graph(graph_def, './tmp/beginner-export',
+                         'beginner-graph.pb', as_text=False)
+
+    # Test trained model
+    y_2 = tf.placeholder("float", [None, 10])
+    correct_prediction2 = tf.equal(tf.argmax(y2, 1), tf.argmax(y_2, 1))
+    accuracy2 = tf.reduce_mean(tf.cast(correct_prediction2, "float"))
+    print(accuracy2.eval({x2: mnist.test.images, y_2: mnist.test.labels}, sess2))

trainer-script/expert.py

@@ -0,0 +1,131 @@
+from __future__ import absolute_import, unicode_literals
+import input_data
+import tensorflow as tf
+import shutil
+import os.path
+
+export_dir = './tmp/expert-export'
+
+if os.path.exists(export_dir):
+    shutil.rmtree(export_dir)
+
+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')
+
+mnist = input_data.read_data_sets("data/", one_hot=True)
+
+g = tf.Graph()
+with g.as_default():
+    x = tf.placeholder("float", shape=[None, 784])
+    y_ = tf.placeholder("float", shape=[None, 10])
+
+    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)
+
+    keep_prob = tf.placeholder("float")
+    h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)
+
+    W_fc2 = weight_variable([1024, 10])
+    b_fc2 = bias_variable([10])
+
+    y_conv = tf.nn.softmax(tf.matmul(h_fc1_drop, W_fc2) + b_fc2)
+
+    cross_entropy = -tf.reduce_sum(y_ * tf.log(y_conv))
+    train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)
+    correct_prediction = tf.equal(tf.argmax(y_conv, 1), tf.argmax(y_, 1))
+    accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))
+
+    sess = tf.Session()
+    sess.run(tf.initialize_all_variables())
+
+    for i in range(20000):
+        batch = mnist.train.next_batch(50)
+        if i % 100 == 0:
+            train_accuracy = accuracy.eval(
+                {x: batch[0], y_: batch[1], keep_prob: 1.0}, sess)
+            print "step %d, training accuracy %g" % (i, train_accuracy)
+        train_step.run(
+            {x: batch[0], y_: batch[1], keep_prob: 0.5}, sess)
+
+    print "test accuracy %g" % accuracy.eval(
+        {x: mnist.test.images, y_: mnist.test.labels, keep_prob: 1.0}, sess)
+
+# Store variable
+_W_conv1 = W_conv1.eval(sess)
+_b_conv1 = b_conv1.eval(sess)
+_W_conv2 = W_conv2.eval(sess)
+_b_conv2 = b_conv2.eval(sess)
+_W_fc1 = W_fc1.eval(sess)
+_b_fc1 = b_fc1.eval(sess)
+_W_fc2 = W_fc2.eval(sess)
+_b_fc2 = b_fc2.eval(sess)
+
+sess.close()
+
+# Create new graph for exporting
+g_2 = tf.Graph()
+with g_2.as_default():
+    x_2 = tf.placeholder("float", shape=[None, 784], name="input")
+
+    W_conv1_2 = tf.constant(_W_conv1, name="constant_W_conv1")
+    b_conv1_2 = tf.constant(_b_conv1, name="constant_b_conv1")
+    x_image_2 = tf.reshape(x_2, [-1, 28, 28, 1])
+    h_conv1_2 = tf.nn.relu(conv2d(x_image_2, W_conv1_2) + b_conv1_2)
+    h_pool1_2 = max_pool_2x2(h_conv1_2)
+
+    W_conv2_2 = tf.constant(_W_conv2, name="constant_W_conv2")
+    b_conv2_2 = tf.constant(_b_conv2, name="constant_b_conv2")
+    h_conv2_2 = tf.nn.relu(conv2d(h_pool1_2, W_conv2_2) + b_conv2_2)
+    h_pool2_2 = max_pool_2x2(h_conv2_2)
+    
+    W_fc1_2 = tf.constant(_W_fc1, name="constant_W_fc1")
+    b_fc1_2 = tf.constant(_b_fc1, name="constant_b_fc1")
+    h_pool2_flat_2 = tf.reshape(h_pool2_2, [-1, 7 * 7 * 64])
+    h_fc1_2 = tf.nn.relu(tf.matmul(h_pool2_flat_2, W_fc1_2) + b_fc1_2)
+
+    W_fc2_2 = tf.constant(_W_fc2, name="constant_W_fc2")
+    b_fc2_2 = tf.constant(_b_fc2, name="constant_b_fc2")
+
+    # DropOut is skipped for exported graph.
+    
+    y_conv_2 = tf.nn.softmax(tf.matmul(h_fc1_2, W_fc2_2) + b_fc2_2, name="output")
+    
+    sess_2 = tf.Session()
+    init_2 = tf.initialize_all_variables();
+    sess_2.run(init_2)
+
+    graph_def = g_2.as_graph_def()
+    tf.train.write_graph(graph_def, export_dir, 'expert-graph.pb', as_text=False)
+
+    # Test trained model
+    y__2 = tf.placeholder("float", [None, 10])
+    correct_prediction_2 = tf.equal(tf.argmax(y_conv_2, 1), tf.argmax(y__2, 1))
+    accuracy_2 = tf.reduce_mean(tf.cast(correct_prediction_2, "float"))
+
+    print "check accuracy %g" % accuracy_2.eval(
+        {x_2: mnist.test.images, y__2: mnist.test.labels}, sess_2)