import numpy as np from sklearn import datasets import matplotlib.pyplot as plt from neupy import algorithms, layers, environment environment.reproducible() environment.speedup() mnist = datasets.fetch_mldata('MNIST original') data = (mnist.data / 255.).astype(np.float32) np.random.shuffle(data) x_train, x_test = data[:60000], data[60000:] x_train_4d = x_train.reshape((60000, 1, 28, 28)) x_test_4d = x_test.reshape((10000, 1, 28, 28)) conv_autoencoder = algorithms.Momentum( [ layers.Input((1, 28, 28)), layers.Convolution((16, 3, 3)) > layers.Relu(), layers.Convolution((16, 3, 3)) > layers.Relu(), layers.MaxPooling((2, 2)), layers.Convolution((32, 3, 3)) > layers.Relu(), layers.MaxPooling((2, 2)), layers.Reshape(),
import numpy as np from sklearn.model_selection import train_test_split from neupy import algorithms, layers, environment from neupy.datasets import reber environment.reproducible() environment.speedup() def add_padding(data): n_sampels = len(data) max_seq_length = max(map(len, data)) data_matrix = np.zeros((n_sampels, max_seq_length)) for i, sample in enumerate(data): data_matrix[i, -len(sample):] = sample return data_matrix # An example of possible values for the `data` and `labels` # variables # # >>> data # array([array([1, 3, 1, 4]), # array([0, 3, 0, 3, 0, 4, 3, 0, 4, 4]), # array([0, 3, 0, 0, 3, 0, 4, 2, 4, 1, 0, 4, 0])], dtype=object) # >>> # >>> labels # array([1, 0, 0])
def test_speedup_environment(self): environment.speedup() self.assertEqual(theano.config.floatX, 'float32') self.assertEqual(theano.config.allow_gc, False)