import matplotlib.pyplot as plt
import numpy as np
import pylab
import matplotlib.cm as cm
import Image
import load_class
load = load_class.load(1)
# f = pylab.figure()
# for n, fname in enumerate(('1.png', '2.png')):
# image=Image.open(fname).convert("L")
# arr=np.asarray(image)
# f.add_subplot(2, 1, n) # this line outputs images on top of each other
# # f.add_subplot(1, 2, n) # this line outputs images side-by-side
# pylab.imshow(arr,cmap=cm.Greys_r)
# pylab.title('Double image')
# pylab.show()
x_validate, labels_validate = load.load_validation_set()
class_to_filter=0
aantal = 0
for i, j in enumerate(labels_validate[0:1000]):
if j == class_to_filter:
f = pylab.figure()
for k in range(12):
f.add_subplot(3,4,k+1)
indices=[]
for index in np.arange(len(inputs)):
if ( oneshot_class > -1 and i < len(oneshot_indices[oneshot_class]) and index == oneshot_indices[oneshot_class][i]):
i+=1
#print("Index {} overeenkomstig label {}".format(index,targets[index]));sys.stdout.flush()
else:
indices.append(index)
if shuffle:
np.random.shuffle(indices)
for start_idx in range(0, len(indices) - batch_size + 1, batch_size):
excerpt = indices[start_idx:start_idx + batch_size]
yield inputs[excerpt], targets[excerpt]
import load_class
load = load_class.load(data_ratio)
import convnet
convnet = convnet.convnet(20)
try:
i = 0
path = "/home/jasper/oneshot-gestures/"
while os.path.exists("{}output/acc-cost_{}.csv".format(path,i)):
i += 1
fo1 = open("{}output/acc-cost_{}.csv".format(path,i), "w")
fo1.write("training_loss;validation_loss;validation_accuracy;epoch_time\n")
except IOError as e:
print("I/O error({0}): {1}".format(e.errno, e.strerror))
raise
except:
if ( i < len(oneshot_indices[oneshot_class]) and index == oneshot_indices[oneshot_class][i]):
i+=1
else:
indices.append(index)
if shuffle:
np.random.shuffle(indices)
for start_idx in range(0, len(indices) - batch_size + 1, batch_size):
excerpt = indices[start_idx:start_idx + batch_size]
yield inputs[excerpt], targets[excerpt]
base_dir_path = "/home/jasper/oneshot-gestures/"
test_accuracies = []
load = load_class.load(size_ratio=1.0)
# Load data
x_validate, labels_validate, indices_validate = load.load_validation_set()
x_train, labels_train, indices_train = load.load_training_set()
x_test, labels_test, indices_test = load.load_testing_set()
convnet = convnet.convnet(num_output_units=20)
convnet.save_param_values("{}/default_param".format(base_dir_path))
for oneshot_class in xrange(20
):
print("Learning gestures excluding class {}".format(oneshot_class))
save_param_patch = "{}convnet_params/param-excl-class-{}".format(base_dir_path,oneshot_class)
convnet.load_param_values(save_param_patch)
