diff options
Diffstat (limited to 'main.py')
| -rw-r--r-- | main.py | 76 |
1 files changed, 53 insertions, 23 deletions
@@ -28,8 +28,8 @@ class Layer(): self.grad_weights = np.zeros((output_size, input_size)) # Bias - # def __repr__(self): - # return "(Layer {} -> {})".format(len(self.weights), len(self.bias)) + def __repr__(self): + return "<Layer {} -> {}>".format(self.weights.shape[1], self.weights.shape[0]) def compute(self, input): self.input = input @@ -54,6 +54,24 @@ class MultiLayerPerceptron(): for k in range(len(layer_sizes)-1): self.layers.append(Layer(layer_sizes[k], layer_sizes[k+1])) + def __repr__(self): + return "<MultiLayerPerceptron {}>".format(self.layers) + + @staticmethod + def load(filename): + array = np.load(open(filename, 'rb')) + sizes = [array[0].shape[1]] + for layer in array: + sizes.append(layer.shape[0]) + mlp = MultiLayerPerceptron(sizes) + for k, layer in enumerate(array): + mlp.layers[k].weights = layer + return mlp + + def save(self, filename): + array = [layer.weights for layer in self.layers] + np.save(open(filename, 'wb'), array) + def error(self, output, expected): return 1/2 * np.sum((expected - output)**2) @@ -73,22 +91,34 @@ class MultiLayerPerceptron(): for layer in self.layers: layer.weights -= learning_rate * layer.grad_weights - def learn(self, space, test_space, iterations=100, test_iterations=100, epochs=100): + def test(self, test_space, test_iterations=100): + errors = [] + success = 0 + for j in range(test_iterations): + input, expected = random.choice(test_space) + output = self.frontprop(input) + errors.append(self.error(output, expected)) + if np.argmax(output) == np.argmax(expected): + success += 1 + return sum(errors)/100, success/100 + + def learn(self, space, test_space, iterations=100, test_iterations=100, epochs=100, save=None): mean_errors = [] mean_accuracy = [] begin = time.time() for i in range(epochs): + if save: self.save(save) # Print ETA elapsed = time.time() - begin total = int(epochs/i * elapsed) if i > 0 else 0 remaining = int(total - elapsed) - text = "\r[Learning] {:3.2f}% ~ ert ".format(i*epochs/100) - if remaining//3600 > 0: - text += "{:2d} h ".format(remaining//3600) - remaining = remaining % 3600 - if remaining//60 > 0: - text += "{:2d} m ".format(remaining//60) - remaining = remaining % 60 + text = "\r[Learning] {:3.2f}% ~ ert ".format(i*100/epochs) + # if remaining//3600 > 0: + text += "{:2d} h ".format(remaining//3600) + remaining = remaining % 3600 + # if remaining//60 > 0: + text += "{:2d} m ".format(remaining//60) + remaining = remaining % 60 text += "{:2d} s".format(remaining) print(text, end="") # Train @@ -97,16 +127,9 @@ class MultiLayerPerceptron(): self.backprop(input, expected) self.fit() # Test - errors = [] - success = 0 - for j in range(test_iterations): - input, expected = random.choice(test_space) - errors.append(self.error(self.frontprop(input), expected)) - # Only for classifier - if np.argmax(self.frontprop(input)) == np.argmax(expected): - success += 1 - mean_errors.append(sum(errors, 0)/100) - mean_accuracy.append(success/100) + error, accuracy = self.test(test_space, test_iterations) + mean_errors.append(error) + mean_accuracy.append(accuracy) return mean_errors, mean_accuracy @@ -143,7 +166,8 @@ if __name__ == "__main__": # MNIST print("[Initializing] Perceptron: ", end="") - mlp = MultiLayerPerceptron([28*28, 32, 16, 10]) + # mlp = MultiLayerPerceptron([28*28, 32, 16, 10]) + mlp = MultiLayerPerceptron.load('mnist.npy') print("OK") print("[Loading] Training set: ", end="") space = np.load(open("data/train.npy", "rb")) @@ -153,13 +177,19 @@ if __name__ == "__main__": test_space = [(input.reshape(28*28), expected) for input, expected in test_space] print("OK") - mean_errors, mean_accuracy = mlp.learn(space, test_space) + mean_errors, mean_accuracy = mlp.learn(space, test_space, iterations=1000, epochs=100, save='mnist.npy') image, expected = random.choice(space) input = np.reshape(image, 28*28) print(mlp.frontprop(input), expected) - print(np.argmax(mlp.frontprop(input)), np.argmax(expected)) + print(np.argmax(mlp.frontprop(input)), "=>", np.argmax(expected)) + plt.figure(1) + plt.subplot(211) plt.plot(mean_errors) + plt.ylabel("error") + plt.subplot(212) plt.plot(mean_accuracy) + plt.ylabel("accuracy") + plt.axis([0, len(mean_accuracy), 0, 1]) plt.show()
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