Created
May 10, 2018 19:54
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mlp with l2 reg
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import numpy as np | |
import os | |
import dtdata as dt | |
from sklearn.model_selection import train_test_split | |
from sklearn.decomposition import PCA | |
from keras.models import Sequential | |
from keras.layers import Dense, Activation, Dropout | |
from keras.optimizers import RMSprop, Adam | |
from keras.callbacks import ModelCheckpoint | |
from keras import regularizers | |
# fix random seed for reproducibility | |
np.random.seed(90210) | |
num_classes = 5 | |
batch_size = 256 | |
epochs = 400 | |
input_size = 512 | |
subset = -1 # -1 to use the entire data set | |
savePath = r'/home/suroot/Documents/train/daytrader/' | |
path =r'/home/suroot/Documents/train/daytrader/ema-crossover' # path to data | |
(data, labels_classed) = dt.cacheLoadData(path, num_classes, input_size) | |
x_train, x_test, y_train, y_test = train_test_split(data, labels_classed, test_size=0.1) | |
model = Sequential() | |
model.add(Dense(128, activation='relu', input_dim=input_size, kernel_regularizer=regularizers.l2(0.01))) | |
model.add(Dropout(0.4)) | |
model.add(Dense(64, activation='relu', kernel_regularizer=regularizers.l2(0.01))) | |
model.add(Dropout(0.5)) | |
model.add(Dense(num_classes, activation='softmax')) | |
model.summary() | |
model.compile(loss='categorical_crossentropy', | |
optimizer=Adam(lr=0.001, beta_1=0.9, beta_2=0.999, epsilon=None, decay=0.0, amsgrad=False), | |
metrics=['accuracy']) | |
# checkpoint | |
modelPath= savePath+"mlp-{epoch:02d}-{val_acc:.2f}.hdf5" | |
checkpoint = ModelCheckpoint(modelPath, monitor='val_acc', verbose=1, save_best_only=True, mode='max') | |
history = model.fit(x_train, y_train, | |
batch_size=batch_size, | |
epochs=epochs, | |
verbose=2, | |
callbacks=[checkpoint], | |
validation_data=(x_test, y_test)) | |
score = model.evaluate(x_test, y_test, verbose=0) | |
print('Test loss:', score[0]) | |
print('Test accuracy:', score[1]) | |
dt.plotHistory(history) |
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