__author__ = "greg" import loader import neural_network training_data, validation_data, test_data = loader.load_data_wrapper() net = neural_network.Network([784, 30, 10]) net.SGD(training_data, 30, 10, 3.0, test_data=test_data)
# ---------------------- # - read the input data: import loader import matrix as mtx training_data, validation_data, test_data = loader.load_data_wrapper() training_data = list(training_data) net = mtx.Matrix([784, 100, 10]) net.learn(training_data, 30, 10, 5.0, test_data)
def train_network(net): # Load training data training_data, validation_data, test_data = loader.load_data_wrapper() # Train neural network net.SGD(training_data, 30, 10, 3.0, test_data=test_data)
def evaluate(self, data):
test_results = [(np.argmax(self.feedforward(x)), np.argmax(y))
for (x, y) in data]
return sum(int(x == y) for (x, y) in test_results)
def save(self, filename):
data = {
"sizes": self.sizes,
"weights": [w.tolist() for w in self.weights],
"biases": [b.tolist() for b in self.biases],
"cost": str(self.cost.__name__)
}
f = open(filename, "w")
json.dump(data, f)
f.close()
def sigmoid(z):
return 1.0 / (1.0 + np.exp(-z))
def sigmoid_prime(z):
return sigmoid(z) * (1 - sigmoid(z))
if __name__ == '__main__':
tr, va, te = loader.load_data_wrapper()
network = Network([784, 30, 10])
network.SGD(tr, 30, 10, 0.5, 5.0, va, True, True, True, True)
network.save("improved_network_results.json")
import numpy as np
from keras.models import Sequential
from keras.layers import Dense
from loader import load_data_wrapper
train, valid, test = load_data_wrapper()
model = Sequential()
model.add(Dense(784, input_dim=784, activation='relu'))
model.add(Dense(30, activation='relu'))
model.add(Dense(10, activation='softmax'))
model.compile(loss='categorical_crossentropy',
optimizer='adam',
metrics=['accuracy'])
print("finished compiling")
inp = []
out = []
count = 0
for i, o in train:
if len(inp) > 8000:
break
base_i = []
base_o = []
for item in i:
base_i.append(item[0])
for item in o:
