Renamed data creation script and wrote script to parse IAM. Getting a float32 cast error at the end. Unknown reason.

hwRecProto2.py

@@ -0,0 +1,113 @@
+########################################################################################
+# Author: Thomas Flucke
+# Date: 2017-05-13
+
+# Abreviations:
+# vect = Vector
+# ANN  = Artifical Neural Network
+# corr  = Correct version
+
+########################################################################################
+# Declare the data format
+
+import random
+
+class Datum:
+    def __init__(self, label, img):
+        self.label = [0] * (10 + 26 + 26)
+        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, (10 + 26 + 26) + 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
+
+########################################################################################
+# Load IAM dataset
+
+import cPickle as pickle
+
+with open('iamDataset.obj', 'rb') as input:
+    iam = pickle.load(input)
+
+########################################################################################
+# Import tensorflow library and define AAN
+
+import tensorflow as tf
+LEARNING_CONST = 0.5
+
+# Create a tensorflow placeholder for an array [nx784] float32's (a.k.a. n MNIST vectors)
+inVect = tf.placeholder(tf.float32, [None, 972])
+# Initalize the ANN with zero's
+# Define tensorflow variable for the weight matrix [784x10] so we can matrix multiply
+weights = tf.Variable(tf.zeros([972, (10 + 26 + 26)]))
+# Define tensorflow variable for the bias vector
+biases = tf.Variable(tf.zeros([(10 + 26 + 26)]))
+# Define formula for calculating output [nx10]
+outVect = tf.nn.softmax(tf.matmul(inVect, weights) + biases)
+# Create a tensorflow placeholder for the correct answer vector
+outVectCorr = tf.placeholder(tf.float32, [None, (10 + 26 + 26)])
+
+# Calculate how incorrect the solutions arrive were
+crossEntropy = tf.reduce_mean(
+    -tf.reduce_sum(
+        outVectCorr * tf.log(outVect),
+        # Tells reduce_sum to use the 10-length array, and not the n-length
+        reduction_indices=[1]
+    )
+)
+
+trainStep = tf.train.GradientDescentOptimizer(LEARNING_CONST).minimize(crossEntropy)
+
+########################################################################################
+# Define accuracy checking conditions
+
+# Define formula for determining correctness
+# Highest value in outVect 1st index == highest value in correct outVect 1st index
+predictionCorr = tf.equal(tf.argmax(outVect, 1), tf.argmax(outVectCorr, 1))
+
+# Calculate how accurate the system was
+accuracy = tf.reduce_mean(tf.cast(predictionCorr, tf.float32))
+
+########################################################################################
+# Run the system
+
+# Create interactive session
+sess = tf.InteractiveSession()
+
+# Initialize variables
+tf.global_variables_initializer().run()
+
+for _ in range(1000) :
+    # Get 100 random digits from training set
+    batchIns, batchOuts = iam.nextBatch(100)
+    # Run the training step in the interactive session with the given inputs/outputs
+    sess.run(trainStep, feed_dict={inVect: batchIns, outVectCorr: batchOuts})
+
+# Check accuracy
+print(sess.run(accuracy, feed_dict={inVect: (o.img for o in iam.test), outVectCorr: (o.label for o in iam.test)}))

png2Vect2.py → png2Obj.py

@@ -34,6 +34,7 @@ class IAM:
                 img = scipy.ndimage.imread(IMG_TEMPLATE % (x, f), True)
                 img = scipy.misc.imresize(img, 0.03)
                 img = list(itertools.chain.from_iterable(img))
+                print len(img)
                 if len(self.test) < (5 * x):
                     self.test.append(Datum(x, img))
                 else: