def evaluate_from_file(filename):
## read mnist data
# training_data, validation_data, test_data = \
# mnist_loader_with_pickle.load_data_wrapper()
## load training data of ball from image
# ball_data = read.load_ball()
# training_data = training_data + ball_data
###########################################
## create network ##
## input layer: 784 (28 * 28) ##
## hidden layer: 30 * 1 ##
## output layer: 11 (digits and ball) ##
###########################################
# net = network.Network([784, 30, 11])
net = network.Network([784, 100, 10])
## execute training
# net.SGD(training_data, 30, 10, 3.0, test_data=test_data)
# net.save_data()
# net.evaluate(test_data)
net.load_data()
data = read.load_image_rgb(filename, 0)
ret = net.evaluate_data(data)
return ret
def evaluate_from_file(filename):
## read mnist data
#training_data, validation_data, test_data = \
# mnist_loader_with_pickle.load_data_wrapper()
## load training data of ball from image
#ball_data = read.load_ball()
#training_data = training_data + ball_data
###########################################
## create network ##
## input layer: 784 (28 * 28) ##
## hidden layer: 30 * 1 ##
## output layer: 11 (digits and ball) ##
###########################################
net = network.Network([320 * 240, 30, 2])
## execute training
#net.SGD(training_data, 30, 10, 3.0, test_data=test_data)
#net.save_data()
#net.evaluate(test_data)
net.load_data()
data = read.load_image_rgb(filename, 0)
ret = net.evaluate_data(data)
return ret
def evaluate_from_file(filename):
###########################################
## create network ##
## input layer: 784 (28 * 28) ##
## hidden layer: 30 * 1 ##
## output layer: 10 (digits) ##
###########################################
net = network.Network([784, 100, 10])
## execute training
#net.SGD(training_data, 30, 10, 3.0, test_data=test_data)
#net.save_data()
#net.evaluate(test_data)
net.load_data()
data = read.load_image_rgb(filename, 0)
ret = net.evaluate_data(data)
return ret
def evaluate_from_file(filename):
###########################################
## create network ##
## input layer: 784 (28 * 28) ##
## hidden layer: 30 * 1 ##
## output layer: 10 (digits) ##
###########################################
net = network.Network([784, 100, 10])
## execute training
#net.SGD(training_data, 30, 10, 3.0, test_data=test_data)
#net.save_data()
#net.evaluate(test_data)
net.load_data()
data = read.load_image_rgb(filename, 0)
ret = net.evaluate_data(data)
return ret
