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Ben
46 lines
1.4 KiB
Python
46 lines
1.4 KiB
Python
import numpy as np
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from keras.datasets import mnist
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from keras.layers import Activation
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from keras.layers import Dense
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from keras.models import Sequential
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from keras.utils import np_utils
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np.random.seed(1337)
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nb_classes = 10
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batch_size = 128
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nb_epoch = 5
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weighted_class = 9
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standard_weight = 1
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high_weight = 5
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max_train_samples = 5000
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max_test_samples = 1000
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def get_data():
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# the data, shuffled and split between tran and test sets
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(X_train, y_train), (X_test, y_test) = mnist.load_data()
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X_train = X_train.reshape(60000, 784)[:max_train_samples]
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X_test = X_test.reshape(10000, 784)[:max_test_samples]
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X_train = X_train.astype('float32') / 255
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X_test = X_test.astype('float32') / 255
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# convert class vectors to binary class matrices
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y_train = y_train[:max_train_samples]
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y_test = y_test[:max_test_samples]
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Y_train = np_utils.to_categorical(y_train, nb_classes)
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Y_test = np_utils.to_categorical(y_test, nb_classes)
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test_ids = np.where(y_test == np.array(weighted_class))[0]
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return (X_train, Y_train), (X_test, Y_test), test_ids
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def validate_regularizer(weight_reg=None, activity_reg=None):
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model = Sequential()
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model.add(Dense(50, input_shape=(784,)))
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model.add(Activation('relu'))
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model.add(Dense(10, W_regularizer=weight_reg,
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activity_regularizer=activity_reg))
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model.add(Activation('softmax'))
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return model
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