wehub-resource-sync
79 lines
2.5 KiB
Python
79 lines
2.5 KiB
Python
import logging
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from itertools import combinations, islice
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import numpy as np
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try:
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from sklearn.model_selection import train_test_split
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except ImportError:
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from sklearn.cross_validation import train_test_split
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from mla.metrics import accuracy
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from mla.neuralnet import NeuralNet
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from mla.neuralnet.layers import Activation, TimeDistributedDense
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from mla.neuralnet.layers.recurrent import LSTM
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from mla.neuralnet.optimizers import Adam
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logging.basicConfig(level=logging.DEBUG)
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def addition_dataset(dim=10, n_samples=10000, batch_size=64):
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"""Generate binary addition dataset.
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http://devankuleindiren.com/Projects/rnn_arithmetic.php
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"""
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binary_format = "{:0" + str(dim) + "b}"
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# Generate all possible number combinations
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combs = list(islice(combinations(range(2 ** (dim - 1)), 2), n_samples))
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# Initialize empty arrays
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X = np.zeros((len(combs), dim, 2), dtype=np.uint8)
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y = np.zeros((len(combs), dim, 1), dtype=np.uint8)
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for i, (a, b) in enumerate(combs):
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# Convert numbers to binary format
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X[i, :, 0] = list(reversed([int(x) for x in binary_format.format(a)]))
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X[i, :, 1] = list(reversed([int(x) for x in binary_format.format(b)]))
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# Generate target variable (a+b)
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y[i, :, 0] = list(reversed([int(x) for x in binary_format.format(a + b)]))
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X_train, X_test, y_train, y_test = train_test_split(
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X, y, test_size=0.2, random_state=1111
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)
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# Round number of examples for batch processing
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train_b = (X_train.shape[0] // batch_size) * batch_size
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test_b = (X_test.shape[0] // batch_size) * batch_size
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X_train = X_train[0:train_b]
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y_train = y_train[0:train_b]
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X_test = X_test[0:test_b]
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y_test = y_test[0:test_b]
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return X_train, X_test, y_train, y_test
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def addition_problem(ReccurentLayer):
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X_train, X_test, y_train, y_test = addition_dataset(8, 5000)
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print(X_train.shape, X_test.shape)
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model = NeuralNet(
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layers=[ReccurentLayer, TimeDistributedDense(1), Activation("sigmoid")],
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loss="mse",
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optimizer=Adam(),
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metric="mse",
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batch_size=64,
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max_epochs=15,
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)
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model.fit(X_train, y_train)
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predictions = np.round(model.predict(X_test))
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predictions = np.packbits(predictions.astype(np.uint8))
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y_test = np.packbits(y_test.astype(np.int))
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print(accuracy(y_test, predictions))
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# RNN
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# addition_problem(RNN(16, parameters=Parameters(constraints={'W': SmallNorm(), 'U': SmallNorm()})))
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# LSTM
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addition_problem(LSTM(16))
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