class MLP_keras:
def __init__(self, learning_rate, layers, functions, optimizer_name,
beta=0.0, dropout=1.0):
self.n_input = layers[0]
self.n_hidden = layers[1:-1]
self.n_output = layers[-1]
self.model = Sequential()
if len(self.n_hidden) == 0:
# single layer
self.model.add(Dense(self.n_output, activation=functions[0],
kernel_regularizer=regularizers.l2(beta),
input_shape=(self.n_input,)))
elif len(self.n_hidden) == 1:
# hidden layer
self.model.add(Dense(self.n_hidden[0], activation=functions[0],
kernel_regularizer=regularizers.l2(beta),
input_shape=(self.n_input,)))
self.model.add(Dropout(dropout))
# output layer
self.model.add(Dense(self.n_output, activation=functions[1],
kernel_regularizer=regularizers.l2(beta)))
else:
# the first hidden layer
self.model.add(Dense(self.n_hidden[0], activation=functions[0],
kernel_regularizer=regularizers.l2(beta),
input_shape=(self.n_input,)))
self.model.add(Dropout(dropout))
# the second hidden layer
self.model.add(Dense(self.n_hidden[1], activation=functions[1],
kernel_regularizer=regularizers.l2(beta)))
self.model.add(Dropout(dropout))
# the output layer
self.model.add(Dense(self.n_output, activation=functions[2],
kernel_regularizer=regularizers.l2(beta)))
self.model.summary()
if optimizer_name == 'Adam': optimizer = Adam(learning_rate)
#self.model.compile(loss='mean_squared_error',
# optimizer=optimizer,
# metrics=['accuracy'])
self.model.compile(loss='categorical_crossentropy',
optimizer=optimizer,
metrics=['accuracy'])
def train(self, epochs, trn, vld=None, batch_size=32, es=0):
if vld is not None:
validation_data = (vld.x, vld.y)
if es > 0:
callbacks = [EarlyStopping(monitor='val_loss', patience=es)]
else:
callbacks = None
else:
validation_data = None
callbacks = None
self.model.fit(trn.x, trn.y,
batch_size=batch_size,
epochs=epochs,
verbose=2,
callbacks=callbacks,
validation_data=validation_data)
loss, tst_acc = self.model.evaluate(trn.x, trn.y, verbose=0)
if vld is not None:
_, vld_acc = self.model.evaluate(vld.x, vld.y, verbose=0)
else:
vld_acc = 0
return ['', loss, tst_acc, vld_acc, 0]
def evaluate(self, x_test, y_test):
score = self.model.evaluate(x_test, y_test, verbose=0)
print('Test loss:', score[0])
print('Test accuracy:', score[1])
def score(self, tst):
return self.model.evaluate(tst.x, tst.y, verbose=0)[1]
def predict_proba(self, x):
if x is None: return None
return self.model.predict_proba(x, verbose=0)
model = load_model(savefile)
else:
model = Sequential()
model.add(Dense(128, input_dim=input_dimension, kernel_initializer=hidden_initializer, activation='relu'))
#model.add(Dense(128, input_dim=input_dimension, activation='relu'))
model.add(Dropout(dropout_rate))
model.add(Dense(64, kernel_initializer=hidden_initializer, activation='relu'))
model.add(Dense(2, kernel_initializer=hidden_initializer, activation='softmax'))
#model.add(Dense(64, activation='relu'))
#model.add(Dense(2, activation='softmax'))
sgd = SGD(lr=learning_rate, momentum=momentum)
model.compile(loss='binary_crossentropy', optimizer=sgd, metrics=['acc'])
model.fit(X_train, y_train, epochs=50, batch_size=128)
predictions = model.predict_proba(X_test)
ans = pd.DataFrame(predictions,columns=['target','dmy'])
print ans
outdf = pd.concat([outdf,ans['target']],axis=1)
outdf.to_csv('./submit_keras.csv',index=False)
#save the model
#model_json = model.to_json()
#with open("./ans.json", "w") as json_file:
# json_file.write(model_json)
model.save(savefile)