Changed to take in reformatted IAM data

iam_classifier.py

@@ -0,0 +1,176 @@
+# From the tutorial on 
+# https://www.tensorflow.org/get_started/mnist/pros
+
+import argparse
+import sys
+
+########################################################################################
+# Declare the data format
+
+import random
+import _pickle as pickle
+
+N_INPUT = 972
+N_OUTPUT = 10 + 26 + 26
+
+class Datum:
+    def __init__(self, label, img):
+        self.label = [0] * N_OUTPUT
+        self.label[label - 1] = 1
+        self.img = img
+
+class IAM:
+    def __init__(self):
+        print("Building dataset...")
+        self.train = []
+        self.test = []
+        for x in range(1, N_OUTPUT + 1):
+            print("Preparing sample %d..." % x)
+            for f in os.listdir(DATA_FOLDER % x):
+                img = scipy.ndimage.imread(IMG_TEMPLATE % (x, f), True)
+                img = scipy.misc.imresize(img, 0.03)
+                img = list(itertools.chain.from_iterable(img))
+                if len(self.test) < (5 * x):
+                    self.test.append(Datum(x, img))
+                else:
+                    self.train.append(Datum(x, img))
+    def nextBatch(self, size):
+        used = []
+        res = []
+        labels = []
+        while len(used) < size:
+            i = random.randint(0, len(self.train) - 1)
+            if i in used:
+                continue
+            else:
+                used.append(i)
+                res.append(self.train[i].img)
+                labels.append(self.train[i].label)
+        return res, labels
+    def testSet(self):
+        res = []
+        labels = []
+        for i in range(0, len(self.test)):
+            res.append(self.test[i].img)
+            labels.append(self.test[i].label)
+        return res, labels
+
+print(sys.argv)
+
+LEARNING_CONST = 0.05
+TRAIN_CYCLES = 1000
+BATCH_SIZE = 100
+
+# Turn off GPU Warnings/All other warnings
+import os
+os.environ['TF_CPP_MIN_LOG_LEVEL']='2'
+
+
+import tensorflow as tf
+
+FLAGS = None
+
+
+def deepnn(x):
+  # Reshape to use within a convolutional neural net.
+  x_image = tf.reshape(x, [-1, 18, 54, 1]) # tf.reshape(x, [-1, 28, 28, 1])
+
+  # First convolutional layer - maps one grayscale image to 32 feature maps.
+  W_conv1 = weight_variable([5, 5, 1, 32])
+  b_conv1 = bias_variable([32])
+  h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1)
+
+  # Pooling layer - downsamples by 2X.
+  h_pool1 = max_pool_2x2(h_conv1)
+
+  # Second convolutional layer -- maps 32 feature maps to 64.
+  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)
+
+  # Second pooling layer.
+  h_pool2 = max_pool_2x2(h_conv2)
+
+  # Fully connected layer 1 
+  W_fc1 = weight_variable([7 * 10 * 64, 1024]) # weight_variable([7 * 7 * 64, 1024])
+  b_fc1 = bias_variable([1024])
+
+  h_pool2_flat = tf.reshape(h_pool2, [-1, 7*10*64]) # tf.reshape(h_pool2, [-1, 7*7*64])
+  h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1)
+
+  # Dropout - controls the complexity of the model, prevents co-adaptation of
+  # features.
+  keep_prob = tf.placeholder(tf.float32)
+  h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)
+
+  # Map the 1024 features to 62 classes, one for each symbol
+  W_fc2 = weight_variable([1024, 62]) # weight_variable([1024, 10])
+  b_fc2 = bias_variable([62]) # bias_variable([10])
+
+  y_conv = tf.matmul(h_fc1_drop, W_fc2) + b_fc2
+  return y_conv, keep_prob
+
+
+def conv2d(x, W):
+  """conv2d returns a 2d convolution layer with full stride."""
+  return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME')
+
+
+def max_pool_2x2(x):
+  """max_pool_2x2 downsamples a feature map by 2X."""
+  return tf.nn.max_pool(x, ksize=[1, 2, 2, 1],
+                        strides=[1, 2, 2, 1], padding='SAME')
+
+
+def weight_variable(shape):
+  """weight_variable generates a weight variable of a given shape."""
+  initial = tf.truncated_normal(shape, stddev=0.1)
+  return tf.Variable(initial)
+
+
+def bias_variable(shape):
+  """bias_variable generates a bias variable of a given shape."""
+  initial = tf.constant(0.1, shape=shape)
+  return tf.Variable(initial)
+
+
+def main(_):
+  # Import data
+  # mnist = input_data.read_data_sets(FLAGS.data_dir, one_hot=True)
+
+  with open('iamDataset.obj', 'rb') as input:
+    iam = pickle.load(input)
+
+  # Create the model
+  x = tf.placeholder(tf.float32, [None, N_INPUT]) # tf.placeholder(tf.float32, [None, 784])
+
+  # Define loss and optimizer
+  y_ = tf.placeholder(tf.float32, [None, 62]) # tf.placeholder(tf.float32, [None, 10])
+
+  # Build the graph for the deep net
+  y_conv, keep_prob = deepnn(x)
+
+  cross_entropy = tf.reduce_mean(
+      tf.nn.softmax_cross_entropy_with_logits(labels=y_, logits=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, tf.float32))
+
+  with tf.Session() as sess:
+    sess.run(tf.global_variables_initializer())
+    for i in range(101): # 20000
+      batch = iam.nextBatch(BATCH_SIZE) # mnist.train.next_batch(50)
+      if i % 100 == 0:
+        train_accuracy = accuracy.eval(feed_dict={
+            x: batch[0], y_: batch[1], keep_prob: 1.0})
+        print('step %d, training accuracy %g' % (i, train_accuracy))
+      train_step.run(feed_dict={x: batch[0], y_: batch[1], keep_prob: 0.5})
+
+
+if __name__ == '__main__':
+  parser = argparse.ArgumentParser()
+  parser.add_argument('--data_dir', type=str,
+                      default='/tmp/tensorflow/mnist/input_data',
+                      help='Directory for storing input data')
+  FLAGS, unparsed = parser.parse_known_args()
+  tf.app.run(main=main, argv=[sys.argv[0]] + unparsed)