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- import keras
- from keras.models import Sequential
- from keras.layers import Dense, Dropout, Activation
- from keras.optimizers import SGD
- # Generate dummy data
- import numpy as np
- x_train = np.random.random((1000, 20))
- y_train = keras.utils.to_categorical(np.random.randint(10, size=(1000, 1)), num_classes=10)
- x_test = np.random.random((100, 20))
- y_test = keras.utils.to_categorical(np.random.randint(10, size=(100, 1)), num_classes=10)
- model = Sequential()
- # Dense(64) is a fully-connected layer with 64 hidden units.
- # in the first layer, you must specify the expected input data shape:
- # here, 20-dimensional vectors.
- model.add(Dense(64, activation='relu', input_dim=20))
- model.add(Dropout(0.5))
- model.add(Dense(64, activation='relu'))
- model.add(Dropout(0.5))
- model.add(Dense(10, activation='softmax'))
- sgd = SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True)
- model.compile(loss='categorical_crossentropy',
- optimizer=sgd,
- metrics=['accuracy'])
- model.fit(x_train, y_train,
- epochs=3000,
- batch_size=128)
- score = model.evaluate(x_test, y_test, batch_size=128)
- keras.callbacks.EarlyStopping(monitor='val_loss', min_delta=0, patience=0, verbose=0, mode='auto', baseline=None)
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