eriklindernoren--ml-from-scratch
Vuong Nguyen
89 行
3.1 KiB
Python
89 行
3.1 KiB
Python
from __future__ import print_function
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import matplotlib.pyplot as plt
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import numpy as np
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from mlfromscratch.deep_learning import NeuralNetwork
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from mlfromscratch.utils import train_test_split, to_categorical, normalize, Plot
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from mlfromscratch.utils import get_random_subsets, shuffle_data, accuracy_score
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from mlfromscratch.deep_learning.optimizers import StochasticGradientDescent, Adam, RMSprop, Adagrad, Adadelta
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from mlfromscratch.deep_learning.loss_functions import CrossEntropy
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from mlfromscratch.utils.misc import bar_widgets
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from mlfromscratch.deep_learning.layers import RNN, Activation
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def main():
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optimizer = Adam()
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def gen_mult_ser(nums):
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""" Method which generates multiplication series """
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X = np.zeros([nums, 10, 61], dtype=float)
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y = np.zeros([nums, 10, 61], dtype=float)
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for i in range(nums):
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start = np.random.randint(2, 7)
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mult_ser = np.linspace(start, start*10, num=10, dtype=int)
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X[i] = to_categorical(mult_ser, n_col=61)
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y[i] = np.roll(X[i], -1, axis=0)
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y[:, -1, 1] = 1 # Mark endpoint as 1
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return X, y
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def gen_num_seq(nums):
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""" Method which generates sequence of numbers """
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X = np.zeros([nums, 10, 20], dtype=float)
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y = np.zeros([nums, 10, 20], dtype=float)
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for i in range(nums):
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start = np.random.randint(0, 10)
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num_seq = np.arange(start, start+10)
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X[i] = to_categorical(num_seq, n_col=20)
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y[i] = np.roll(X[i], -1, axis=0)
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y[:, -1, 1] = 1 # Mark endpoint as 1
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return X, y
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X, y = gen_mult_ser(3000)
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X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.4)
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# Model definition
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clf = NeuralNetwork(optimizer=optimizer,
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loss=CrossEntropy)
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clf.add(RNN(10, activation="tanh", bptt_trunc=5, input_shape=(10, 61)))
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clf.add(Activation('softmax'))
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clf.summary("RNN")
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# Print a problem instance and the correct solution
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tmp_X = np.argmax(X_train[0], axis=1)
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tmp_y = np.argmax(y_train[0], axis=1)
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print ("Number Series Problem:")
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print ("X = [" + " ".join(tmp_X.astype("str")) + "]")
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print ("y = [" + " ".join(tmp_y.astype("str")) + "]")
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print ()
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train_err, _ = clf.fit(X_train, y_train, n_epochs=500, batch_size=512)
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# Predict labels of the test data
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y_pred = np.argmax(clf.predict(X_test), axis=2)
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y_test = np.argmax(y_test, axis=2)
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print ()
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print ("Results:")
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for i in range(5):
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# Print a problem instance and the correct solution
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tmp_X = np.argmax(X_test[i], axis=1)
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tmp_y1 = y_test[i]
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tmp_y2 = y_pred[i]
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print ("X = [" + " ".join(tmp_X.astype("str")) + "]")
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print ("y_true = [" + " ".join(tmp_y1.astype("str")) + "]")
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print ("y_pred = [" + " ".join(tmp_y2.astype("str")) + "]")
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print ()
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accuracy = np.mean(accuracy_score(y_test, y_pred))
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print ("Accuracy:", accuracy)
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training = plt.plot(range(500), train_err, label="Training Error")
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plt.title("Error Plot")
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plt.ylabel('Training Error')
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plt.xlabel('Iterations')
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plt.show()
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if __name__ == "__main__":
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main() |