class WindPuller(object):
def __init__(self, input_shape, lr=0.01, n_layers=2, n_hidden=8, rate_dropout=0.2, loss=risk_estimation):
print("initializing..., learing rate %s, n_layers %s, n_hidden %s, dropout rate %s." % (
lr, n_layers, n_hidden, rate_dropout))
self.model = Sequential()
self.model.add(Dropout(rate=rate_dropout, input_shape=(input_shape[0], input_shape[1])))
for i in range(0, n_layers - 1):
self.model.add(LSTM(n_hidden * 4, return_sequences=True, activation='tanh',
recurrent_activation='hard_sigmoid', kernel_initializer='glorot_uniform',
recurrent_initializer='orthogonal', bias_initializer='zeros',
dropout=rate_dropout, recurrent_dropout=rate_dropout))
self.model.add(LSTM(n_hidden, return_sequences=False, activation='tanh',
recurrent_activation='hard_sigmoid', kernel_initializer='glorot_uniform',
recurrent_initializer='orthogonal', bias_initializer='zeros',
dropout=rate_dropout, recurrent_dropout=rate_dropout))
self.model.add(Dense(1, kernel_initializer=initializers.glorot_uniform()))
# self.model.add(BatchNormalization(axis=-1, moving_mean_initializer=Constant(value=0.5),
# moving_variance_initializer=Constant(value=0.25)))
self.model.add(BatchNormalization(axis=-1))
self.model.add(Activation("relu(alpha=0., max_value=1.0)"))
opt = RMSprop(lr=lr)
self.model.compile(loss=loss,
optimizer=opt,
metrics=['accuracy'])
def fit(self, x, y, batch_size=32, nb_epoch=100, verbose=1, callbacks=None,
validation_split=0., validation_data=None, shuffle=True,
class_weight=None, sample_weight=None, initial_epoch=0):
self.model.fit(x, y, batch_size, nb_epoch, verbose, callbacks,
validation_split, validation_data, shuffle, class_weight, sample_weight,
initial_epoch)
def save(self, path):
self.model.save(path)
def load_model(self, path):
self.model = load_model(path)
return self
def evaluate(self, x, y, batch_size=32, verbose=1,
sample_weight=None, **kwargs):
return self.model.evaluate(x, y, batch_size, verbose,
sample_weight)
def predict(self, x, batch_size=32, verbose=0):
return self.model.predict(x, batch_size, verbose)
print('x_train shape :', x_train.shape)
print('x_test shape :', x_test.shape)
print('y_train shape :', y_train.shape)
print('y_test shape :', y_test.shape)
batch_size = 32
epochs = 5
model = Sequential()
model.add(Dense(512, input_shape=(max_words, )))
model.add(Activation('relu'))
model.add(Dense(num_classes))
model.add(Activation('softmax'))
model.compile(loss='categorical_crossentropy',
optimizer='adam',
metrics=['accuracy'])
history = model.fit(x_train,
y_train,
batch_size=batch_size,
epochs=epochs,
verbose=1,
validation_split=0.1)
score = model.evaluate(x_test, y_test, batch_size=batch_size, verbose=1)
print('Test score:', score[0])
print('Test accuracy:', score[1])
y_train
uniques, ids = np.unique(
arr, return_inverse=True) # convert 3 words into 0, 1, 2
return to_categorical(ids, len(uniques)) # convert 0, 1, 2 to one-hot
train_y_ohe = one_hot_encode_object_array(train_y)
test_y_ohe = one_hot_encode_object_array(test_y)
model = Sequential()
model.add(Dense(16, input_shape=(4, ))) # each sample has 4 features
model.add(Activation('sigmoid')) # add non-linearity to hidden layer 1
model.add(Dense(3)) # add another 3 neuron final layer
model.add(Activation('softmax')) # give it non-linearity as output
model.summary()
model.compile(optimizer='adam',
loss='categorical_crossentropy',
metrics=["accuracy"])
model.fit(train_X,
train_y_ohe,
validation_split=0.2,
epochs=10,
batch_size=1,
verbose=1)
loss, accuracy = model.evaluate(test_X, test_y_ohe, batch_size=32, verbose=1)
print("Accuracy = {:.2f}".format(accuracy))
from tensorflow.contrib.keras.python.keras.models import Sequential
from tensorflow.contrib.keras.python.keras.layers import Dense, Dropout, Activation
from tensorflow.contrib.keras.python.keras.optimizers import SGD
from tensorflow.contrib.keras.python.keras.utils import to_categorical
import numpy as np
# Generate dummy data
x_train = np.random.random((1000, 20))
y_train = np.random.randint(2, size=(1000, 1))
x_test = np.random.random((100, 20))
y_test = np.random.randint(2, size=(100, 1))
model = Sequential()
model.add(Dense(64, input_dim=20, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(64, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(1, activation='sigmoid'))
model.compile(loss='binary_crossentropy', # binary classification
optimizer='rmsprop',
metrics=['accuracy'])
hist = model.fit(x_train, y_train,
validation_split=0.2,
epochs=1,
batch_size=128)
hist.history
score = model.evaluate(x_test, y_test, batch_size=128)
metrics=['accuracy'])
model.summary()
csv_logger = CSVLogger('training.log')
es_cb = EarlyStopping(monitor='val_loss', mode='auto', patience=30, verbose=1)
tb_cb = TensorBoard(log_dir='./logs', write_graph=True)
hist = model.fit(train,
train_label,
epochs=epochs,
batch_size=batch_size,
validation_split=1 / 7,
shuffle=True,
verbose=1,
callbacks=[es_cb, tb_cb, csv_logger])
score = model.evaluate(test, test_label, verbose=1)
print('test loss:', score[0])
print('test acc:', score[1])
# plot results
plt.subplot(3, 1, 1)
loss = hist.history['loss']
val_loss = hist.history['val_loss']
epochs = len(loss)
plt.plot(range(epochs), loss, marker='.', label='loss')
plt.plot(range(epochs), val_loss, marker='.', label='val_loss')
plt.legend(loc='best')
plt.grid()
plt.xlabel('epoch')
plt.ylabel('loss')
def main():
start = time.time()
# generate multiple time-series sequences
dataframe = generate_sine_data()
dataset = dataframe.values.astype('float32')
# put dataset of multiple time-series sequences
dataset = Normalize(dataset)
# create dataset
length = len(dataset)
train_size = int(length * 0.67)
test_size = length - train_size
train, test = dataset[:train_size], dataset[train_size:]
trainX, trainY = create_dataset(train)
testX, testY = create_dataset(test)
trainX = trainX[len(trainX) % BATCH_SIZE:]
trainY = trainY[len(trainY) % BATCH_SIZE:]
length_test = len(testX)
testX = testX[len(testX) % BATCH_SIZE:]
testY = testY[len(testY) % BATCH_SIZE:]
trainX = np.reshape(trainX, (trainX.shape[0], trainX.shape[1], 3))
testX = np.reshape(testX, (testX.shape[0], testX.shape[1], 3))
# construct the DNN model (LSTM + fully_connected_layer)
model = Sequential()
model.add(LSTM(HIDDEN_SIZE, batch_input_shape=(BATCH_SIZE, Tau, 3)))
model.add(Dense(3))
model.summary()
model.compile(loss='mean_squared_error',
optimizer='adam',
metrics=['accuracy'])
# learn the DNN model on training dataset
hist = model.fit(trainX,
trainY,
batch_size=BATCH_SIZE,
epochs=EPOCHS,
verbose=0,
shuffle=True)
# plot the leargning curve
if PLT:
epochs = range(1, 11)
plt.figure()
plt.plot(epochs, hist.history['loss'], label='loss/training')
plt.plot(epochs, hist.history['acc'], label='acc/training')
plt.xlabel('epoch')
plt.ylabel('acc / loss')
plt.legend()
plt.show()
plt.close()
# evaluate the DNN model on test dataset
score = model.evaluate(testX, testY, batch_size=BATCH_SIZE, verbose=0)
print('loss: {0[0]}, acc: {0[1]} on test dataset'.format(score))
# forecast Ls-steps-ahead value on the test dataset
predicted = model.predict(testX, batch_size=BATCH_SIZE)
# plot testY and predictedY
if PLT:
df_out = pd.DataFrame(predicted[:200])
df_out.columns = [
"predicted_sine", 'predicted_sine_rand', 'predicted_sine_int'
]
df_out = pd.concat([
df_out,
pd.DataFrame(
testY[:200],
columns=["input_sine", "input_sine_rand", "input_sine_int"])
])
plt.figure()
df_out.plot()
plt.show()
plt.close()
# plot the forecasting results on test dataset
if PLT:
plt.ion()
i = 0
while i < 20:
K = 3 # the number of sensor
fig = plt.figure(figsize=(8, K * 4))
plt.subplots_adjust(hspace=0.2)
for j in range(K):
plt.subplot(K, 1, j + 1)
plt.plot(range(i, i + Tau + Ls + 1),
test[length_test % BATCH_SIZE:][i:i + Tau + Ls + 1,
j],
color='silver',
label='original')
plt.plot(range(i, i + Tau),
testX[i, :, j],
color='dodgerblue',
label='input')
plt.scatter(i + Tau + Ls,
predicted[i, j],
s=15,
color='orange',
label='forecast')
plt.legend()
plt.draw()
plt.pause(1.2)
plt.clf()
i += 1
plt.close()
end = time.time()
print('elapsed_time: {}[s]'.format(end - start))
# hist = fit() will record a loss for each epoch
#######################################################
hist1 = model.fit(X, y, batch_size=1, validation_split=0.25,
epochs=10) # accuracy 0.75
hist2 = model.fit(X, y, batch_size=1, epochs=1000) # accuracy 0.75
# See how weights changes to make function more close to xor function
epochs = 5
for epoch in range(epochs):
print("epoch:", epoch)
model.fit(X, y, batch_size=1, epochs=1)
print("Layer1 weights shape:")
print(model.layers[0].weights)
print("Layer1 kernel:")
print(model.layers[0].get_weights()
[0]) # each training, network step closer to xor function
print("Layer1 bias:")
print(model.layers[0].get_weights()[1])
print(model.predict(X))
print(model1.predict(X))
error = model.evaluate([X], [y])
print("error", error)
"""
[[ 0.0033028 ]
[ 0.99581173]
[ 0.99530098]
[ 0.00564186]]
"""
# Model
model = Sequential()
#defaultformat: (samples, rows, col
model.add(
Convolution2D(32,
kernel_size=(3, 3),
strides=(1, 1),
activation='relu',
input_shape=(28, 28, 1)))
model.add(
Convolution2D(32, kernel_size=(3, 3), strides=(1, 1), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(128, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(10, activation='softmax'))
model.summary()
model.compile(loss='categorical_crossentropy',
optimizer='adam',
metrics=['accuracy'])
model.fit(X_train, Y_train, batch_size=32, epochs=10, verbose=1)
score = model.evaluate(X_test, Y_test, verbose=0)
print('Test loss:', score[0])
print('Test accuracy:', score[1])
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)
metrics=['accuracy'])
import numpy as np
x_train = np.random.random((1000, seq_length, 100))
y_train = np.random.randint(2, size=(1000, 1))
x_test = np.random.random((100, seq_length, 100))
y_test = np.random.randint(2, size=(100, 1))
hist = model.fit(x_train,
y_train,
validation_split=0.2,
batch_size=16,
epochs=1)
hist.history
score = model.evaluate(x_test, y_test, batch_size=16) # one batch at a time
"""
(['class Conv1D(tf_convolutional_layers.Conv1D, Layer):\n',
1D convolution layer (e.g. temporal convolution).\n',
'\n',
' This layer creates a convolution kernel that is convolved\n',
' with the layer input over a single spatial (or temporal) dimension\n',
' to produce a tensor of outputs.\n',
' If `use_bias` is True, a bias vector is created and added to the '
'outputs.\n',
' Finally, if `activation` is not `None`,\n',
' it is applied to the outputs as well.\n',
'\n',
' When using this layer as the first layer in a model,\n',
' provide an `input_shape` argument\n',
' (tuple of integers or `None`, e.g.\n',