wehub-resource-sync
83 lines
2.4 KiB
Python
83 lines
2.4 KiB
Python
import os
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import struct
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import numpy as np
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import theano
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from keras.utils import np_utils
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from keras.models import Sequential
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from keras.layers.core import Dense
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from keras.optimizers import SGD
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def load_mnist(path, kind='train'):
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"""Load MNIST data from `path`"""
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labels_path = os.path.join(path,
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'%s-labels-idx1-ubyte'
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% kind)
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images_path = os.path.join(path,
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'%s-images-idx3-ubyte'
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% kind)
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with open(labels_path, 'rb') as lbpath:
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magic, n = struct.unpack('>II',
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lbpath.read(8))
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labels = np.fromfile(lbpath,
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dtype=np.uint8)
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with open(images_path, 'rb') as imgpath:
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magic, num, rows, cols = struct.unpack(">IIII",
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imgpath.read(16))
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images = np.fromfile(imgpath,
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dtype=np.uint8).reshape(len(labels), 784)
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return images, labels
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#### Loading the data
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X_train, y_train = load_mnist('mnist', kind='train')
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X_test, y_test = load_mnist('mnist', kind='t10k')
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#### Preparing the data
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X_train = X_train.astype(theano.config.floatX)
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X_test = X_test.astype(theano.config.floatX)
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y_train_ohe = np_utils.to_categorical(y_train)
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#### Training
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np.random.seed(1)
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model = Sequential()
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model.add(Dense(input_dim=X_train.shape[1],
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output_dim=50,
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init='uniform',
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activation='tanh'))
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model.add(Dense(input_dim=50,
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output_dim=50,
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init='uniform',
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activation='tanh'))
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model.add(Dense(input_dim=50,
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output_dim=y_train_ohe.shape[1],
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init='uniform',
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activation='softmax'))
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sgd = SGD(lr=0.001, decay=1e-7, momentum=.9)
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model.compile(loss='categorical_crossentropy', optimizer=sgd)
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model.fit(X_train, y_train_ohe,
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nb_epoch=50,
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batch_size=300,
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verbose=1,
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validation_split=0.1,
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show_accuracy=True)
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y_train_pred = model.predict_classes(X_train, verbose=0)
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train_acc = np.sum(y_train == y_train_pred, axis=0) / X_train.shape[0]
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print('Training accuracy: %.2f%%' % (train_acc * 100))
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y_test_pred = model.predict_classes(X_test, verbose=0)
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test_acc = np.sum(y_test == y_test_pred, axis=0) / X_test.shape[0]
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print('Test accuracy: %.2f%%' % (test_acc * 100))
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