TRAIN_STEPS = 500 BATCH_SIZE = 50 MNIST = False # For MINST if MNIST: import input_data_minst mnist = input_data_minst.read_data_sets('MNIST_data', one_hot=True) train_images, train_labels = mnist.train.images, mnist.train.labels test_images, test_labels = mnist.test.images, mnist.test.labels else: print "Loading data" train_images, train_labels = input_data.load_train_data() test_images, test_labels = input_data.load_test_data() FLAT_IMG_SIZE = train_images[0].shape[0] NUM_CLASSES = train_labels.shape[1] print "Data loaded" import numpy as np import tensorflow as tf def init_weights(shape): return tf.Variable(tf.random_normal(shape, stddev=0.01)) def model(X, w, w2, w3, w4, w_o, p_keep_conv, p_keep_hidden):
imgs = [] # Need to extend labels to match extra images labels_extended = [] for theta in [20,45,90]: for freq in [.8]: print("Running Gabor",theta,freq) imgs.extend( map(lambda x: apply_filter(x,freq,theta), image_paths) ) labels_extended.extend( labels ) return imgs,labels_extended #Loading data from input_data script train_images,train_labels = input_data.load_train_data(apply_filters) test_images,test_labels = input_data.load_test_data(apply_filters) #Train SVM classifier = svm.SVC(C=.01) classifier.fit(train_images, train_labels) train_score = classifier.score(train_images, train_labels) train_xval_score = cross_validation.cross_val_score(classifier,train_images,train_labels,cv=10,scoring='accuracy') test_score = classifier.score(test_images, test_labels) print("Short Gabors 20-45-90,.8") print("Train score:",train_score) print("Train xval score:",train_xval_score)