import logistic
import rectified_linear
# Layers
import fullyconnected_layer
import input_layer
import mnist_loader_with_pickle as loader
if __name__ == '__main__':
# Load training data and test data
training_data, validation_data, test_data = \
loader.load_data_wrapper()
# Create layers
input_layer = input_layer.InputLayer(784)
input_layer.setData(training_data)
full1 = fullyconnected_layer.FullyConnectedLayer( \
784, 40, logistic.LogisticFunction(), 0.1, 0.9, 0.0)
full2 = fullyconnected_layer.FullyConnectedLayer( \
40, 10, logistic.LogisticFunction(), 0.1, 0.6, 0.0)
# Create network
net = network.Network()
net.appendLayer(input_layer)
net.appendLayer(full1)
net.appendLayer(full2)
# Training start
epoch = 50
net.train(epoch, test_data=test_data)
# Layers
import fullyconnected_layer
import convolution_layer
import maxpooling_layer
import input_layer
import mnist_loader_with_pickle as loader
if __name__ == '__main__':
# Load training data and test data
training_data, validation_data, test_data = \
loader.load_data_wrapper()
# Create layers
input_layer = input_layer.InputLayer(784)
input_layer.setData(training_data[:1000])
conv1 = convolution_layer.ConvolutionLayer( \
(28, 28, 1), (4, 4, 6), (24, 24, 6), logistic.LogisticFunction(), 0.1)
pool1 = maxpooling_layer.MaxPoolingLayer( \
(24, 24, 6), (2, 2), (12, 12, 6))
conv2 = convolution_layer.ConvolutionLayer( \
(12, 12, 6), (4, 4, 14), (8, 8, 14), logistic.LogisticFunction(), 0.1)
pool2 = maxpooling_layer.MaxPoolingLayer( \
(8, 8, 14), (2, 2), (4, 4, 14))
full1 = fullyconnected_layer.FullyConnectedLayer( \
224, 100, logistic.LogisticFunction(), 0.1, 1.0, 0.0)
full2 = fullyconnected_layer.FullyConnectedLayer( \
100, 10, logistic.LogisticFunction(), 0.1, 1.0, 0.0)
# Create network
net = network.Network()
labels = []
labels_vec = []
for x, y in logic_and:
data.append(np.array(x).reshape(-1, 1))
test_data.append(np.array(x).reshape(-1, 1))
label = np.zeros((2, 1))
label[y] = 1.
labels_vec.append(label)
labels.append(y)
inputs = zip(data, labels_vec)
test_inputs= zip(test_data, labels)
####
# Create layers
input_layer = input_layer.InputLayer(2)
input_layer.setData(inputs)
full1 = fullyconnected_layer.FullyConnectedLayer( \
2, 3, logistic.LogisticFunction(), 0.2, 1.0, 0.1)
full2 = fullyconnected_layer.FullyConnectedLayer( \
3, 2, logistic.LogisticFunction(), 0.2, 1.0, 0.1)
# Create network
net = network.Network()
net.appendLayer(input_layer)
net.appendLayer(full1)
net.appendLayer(full2)
epoch = 1000
net.train(epoch, test_data=test_inputs)
