ml-finance-python
python scripts for finance machine learning
git clone https://9o.is/git/ml-finance-python.git
multilayer_perceptron.py
(2441B)
1
2 from __future__ import print_function
3 from sklearn import datasets
4 import matplotlib.pyplot as plt
5 import numpy as np
6
7 # Import helper functions
8 from mlfromscratch.deep_learning import NeuralNetwork
9 from mlfromscratch.utils import train_test_split, to_categorical, normalize, Plot
10 from mlfromscratch.utils import get_random_subsets, shuffle_data, accuracy_score
11 from mlfromscratch.deep_learning.optimizers import StochasticGradientDescent, Adam, RMSprop, Adagrad, Adadelta
12 from mlfromscratch.deep_learning.loss_functions import CrossEntropy
13 from mlfromscratch.utils.misc import bar_widgets
14 from mlfromscratch.deep_learning.layers import Dense, Dropout, Activation
15
16
17 def main():
18
19 optimizer = Adam()
20
21 #-----
22 # MLP
23 #-----
24
25 data = datasets.load_digits()
26 X = data.data
27 y = data.target
28
29 # Convert to one-hot encoding
30 y = to_categorical(y.astype("int"))
31
32 n_samples, n_features = X.shape
33 n_hidden = 512
34
35 X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.4, seed=1)
36
37 clf = NeuralNetwork(optimizer=optimizer,
38 loss=CrossEntropy,
39 validation_data=(X_test, y_test))
40
41 clf.add(Dense(n_hidden, input_shape=(n_features,)))
42 clf.add(Activation('leaky_relu'))
43 clf.add(Dense(n_hidden))
44 clf.add(Activation('leaky_relu'))
45 clf.add(Dropout(0.25))
46 clf.add(Dense(n_hidden))
47 clf.add(Activation('leaky_relu'))
48 clf.add(Dropout(0.25))
49 clf.add(Dense(n_hidden))
50 clf.add(Activation('leaky_relu'))
51 clf.add(Dropout(0.25))
52 clf.add(Dense(10))
53 clf.add(Activation('softmax'))
54
55 print ()
56 clf.summary(name="MLP")
57
58 train_err, val_err = clf.fit(X_train, y_train, n_epochs=50, batch_size=256)
59
60 # Training and validation error plot
61 n = len(train_err)
62 training, = plt.plot(range(n), train_err, label="Training Error")
63 validation, = plt.plot(range(n), val_err, label="Validation Error")
64 plt.legend(handles=[training, validation])
65 plt.title("Error Plot")
66 plt.ylabel('Error')
67 plt.xlabel('Iterations')
68 plt.show()
69
70 _, accuracy = clf.test_on_batch(X_test, y_test)
71 print ("Accuracy:", accuracy)
72
73 # Reduce dimension to 2D using PCA and plot the results
74 y_pred = np.argmax(clf.predict(X_test), axis=1)
75 Plot().plot_in_2d(X_test, y_pred, title="Multilayer Perceptron", accuracy=accuracy, legend_labels=range(10))
76
77
78 if __name__ == "__main__":
79 main()