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003_alexis.py
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003_alexis.py
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import os
import cPickle
import time
import lasagne
import numpy as np
import theano
import theano.tensor as T
import six
import autosklearn.data.competition_data_manager
class FeedForwardNet(object):
def __init__(self, input_shape=(1, 28, 28),
batch_size=100,
num_layers=4,
num_units_per_layer=(10, 10, 10),
dropout_per_layer=(0.5, 0.5, 0.5),
std_per_layer=(0.1, 0.1, 0.1),
num_output_units=10,
dropout_output=0.5,
learning_rate=0.01,
momentum=0.9,
beta1=0.9,
beta2=0.999,
rho=0.95,
solver="sgd"):
assert len(num_units_per_layer) == num_layers - 1
assert len(dropout_per_layer) == num_layers - 1
self.batch_size = int(batch_size)
input_var = T.tensor4('inputs')
target_var = T.ivector('targets')
print("... building network")
print(input_shape)
self.network = lasagne.layers.InputLayer(shape=input_shape,
input_var=input_var)
# Define each layer
for i in range(num_layers - 1):
self.network = lasagne.layers.DenseLayer(
lasagne.layers.dropout(self.network,
p=float(dropout_per_layer[i])),
num_units=int(num_units_per_layer[i]),
W=lasagne.init.Normal(std=float(std_per_layer[i]), mean=0),
b=lasagne.init.Constant(val=0.0),
nonlinearity=lasagne.nonlinearities.rectify)
# Define output layer
self.network = lasagne.layers.DenseLayer(
lasagne.layers.dropout(self.network, p=float(dropout_output)),
num_units=int(num_output_units),
W=lasagne.init.GlorotNormal(),
b=lasagne.init.Constant(),
nonlinearity=lasagne.nonlinearities.softmax)
prediction = lasagne.layers.get_output(self.network)
loss = lasagne.objectives.categorical_crossentropy(prediction,
target_var)
loss = loss.mean()
self.learning_rate = theano.shared(np.array(learning_rate,
dtype=theano.config.floatX))
params = lasagne.layers.get_all_params(self.network, trainable=True)
if solver == "nesterov":
updates = lasagne.updates.nesterov_momentum(loss, params,
learning_rate=learning_rate,
momentum=momentum)
elif solver == "adam":
updates = lasagne.updates.adam(loss, params,
learning_rate=learning_rate,
beta1=beta1, beta2=beta2)
elif solver == "adadelta":
updates = lasagne.updates.adadelta(loss, params,
learning_rate=learning_rate,
rho=rho)
elif solver == "adagrad":
updates = lasagne.updates.adagrad(loss, params,
learning_rate=learning_rate)
elif solver == "sgd":
updates = lasagne.updates.sgd(loss, params,
learning_rate=learning_rate)
elif solver == "momentum":
updates = lasagne.updates.momentum(loss, params,
learning_rate=learning_rate,
momentum=momentum)
else:
updates = lasagne.updates.sgd(loss, params,
learning_rate=learning_rate)
valid_prediction = lasagne.layers.get_output(self.network,
deterministic=True)
valid_loss = lasagne.objectives.categorical_crossentropy(
valid_prediction,
target_var)
valid_loss = valid_loss.mean()
valid_acc = T.mean(T.eq(T.argmax(valid_prediction, axis=1),
target_var),
dtype=theano.config.floatX)
print("... compiling theano functions")
self.train_fn = theano.function([input_var, target_var], loss,
updates=updates,
allow_input_downcast=True)
self.val_fn = theano.function([input_var, target_var],
[valid_loss, valid_acc],
allow_input_downcast=True)
def iterate_minibatches(inputs, targets, batchsize, shuffle=False):
assert len(inputs) == len(targets)
if shuffle:
indices = np.arange(len(inputs))
np.random.shuffle(indices)
for start_idx in range(0, len(inputs) - batchsize + 1, batchsize):
if shuffle:
excerpt = indices[start_idx:start_idx + batchsize]
else:
excerpt = slice(start_idx, start_idx + batchsize)
yield inputs[excerpt], targets[excerpt]
def train(model, X_train, y_train, X_valid, y_valid, num_epochs=500,
lr_policy="inv", gamma=0.1, power=0.5, step=30):
learning_curve = np.zeros([num_epochs])
lr_base = model.learning_rate.get_value()
for epoch in range(num_epochs):
train_err = 0
train_batches = 0
start_time = time.time()
for batch in iterate_minibatches(X_train, y_train, model.batch_size, shuffle=True):
inputs, targets = batch
train_err += model.train_fn(inputs, targets)
train_batches += 1
# And a full pass over the validation data:
val_err = 0
val_acc = 0
val_batches = 0
# Learning rate adaption
if lr_policy == "inv":
decay = np.power((1 + gamma * epoch), (- power))
elif lr_policy == "exp":
decay = np.power(gamma, epoch)
elif lr_policy == "step":
decay = np.power(gamma, (np.floor(epoch / float(step))))
elif lr_policy == "fixed":
decay = 1
decay = np.array([decay], dtype=np.float32)
print ("decay factor %f" % decay)
model.learning_rate.set_value(lr_base * decay)
print ("Learning rate %f" % model.learning_rate.get_value())
for batch in iterate_minibatches(X_valid, y_valid, model.batch_size,
shuffle=False):
inputs, targets = batch
err, acc = model.val_fn(inputs, targets)
val_err += err
val_acc += acc
val_batches += 1
print("Epoch {} of {} took {:.3f}s".format(
epoch + 1, num_epochs, time.time() - start_time))
print(" training loss:\t\t{:.6f}".format(train_err / train_batches))
print(" validation loss:\t\t{:.6f}".format(val_err / val_batches))
print(" validation accuracy:\t\t{:.2f} %".format(
val_acc / val_batches * 100))
learning_curve[epoch] = (val_acc / float(val_batches) * 100)
return 1 - (val_acc / val_batches), learning_curve
dataset = 'alexis'
output = "/home/kleinaa/experiments/automated_deep_learning/automl/alexis/reproduction"
path = os.path.join("/data/aad/automl_data/003", dataset)
dm = autosklearn.data.competition_data_manager.CompetitionDataManager(path)
X_train = dm.data["X_train"].toarray()
X_train = np.array(X_train, dtype=np.float32)
Y_train = np.argmax(np.array(dm.data["Y_train"], dtype=np.float32), axis=1)
X_valid = np.array(dm.data["X_valid"].toarray(), dtype=np.float32)
X_test = np.array(dm.data["X_test"].toarray(), dtype=np.float32)
# Replace the following array by a new ensemble
choices = \
[({'num_layers': 3.0,
'power': 0.9608086215808905,
'solver': '"nesterov"',
'dropout_layer_1': 0.32350553193868387,
'dropout_output': 0.19522565771560596,
'learning_rate': 0.22087420991966428,
'dropout_layer_2': 0.5416862005436668,
'num_units_layer_2': 3429.0,
'batch_size': 963.3316054786878,
'num_units_layer_1': 4742.0,
'step': 17.0,
'momentum': 0.8358157637992942,
'num_epochs': 490.0,
'std_layer_2': 0.002570301444272789,
'lr_policy': 'fixed',
'std_layer_1': 0.00203675415789638,
'gamma': 0.1982457569106505}, 0.02),
({'num_layers': 4.0,
'power': 0.9543822789540591,
'solver': '"nesterov"',
'dropout_layer_1': 0.32350553193868387,
'dropout_output': 0.19522565771560596,
'dropout_layer_3': 0.07073518892061528,
'dropout_layer_2': 0.5416862005436668,
'num_units_layer_2': 3429.0,
'num_units_layer_3': 2921.0,
'batch_size': 963.3316054786878,
'num_units_layer_1': 4742.0,
'step': 17.0,
'std_layer_1': 0.00203675415789638,
'momentum': 0.8358157637992942,
'num_epochs': 490.0,
'std_layer_3': 0.0027860724140478716,
'std_layer_2': 0.002570301444272789,
'lr_policy': 'step',
'learning_rate': 0.22087420991966428,
'gamma': 0.1982457569106505}, 0.02),
({'dropout_output': 0.2219836761125169,
'num_units_layer_4': 503.0,
'num_epochs': 249.0,
'num_units_layer_2': 1798.0,
'num_units_layer_3': 4435.0,
'num_units_layer_1': 5785.0,
'num_layers': 5.0,
'beta2': 0.35170616682914413,
'beta1': 0.3871526867361012,
'std_layer_3': 0.0020693367447187833,
'std_layer_2': 0.01626605159391926,
'std_layer_1': 0.010029613006349311,
'learning_rate': 0.161203081475512,
'std_layer_4': 0.04266796418763915,
'power': 0.7730778282076188,
'dropout_layer_1': 0.5996870640229259,
'dropout_layer_3': 0.016146856157454967,
'dropout_layer_2': 0.049116767959870425,
'dropout_layer_4': 0.6672322817450616,
'batch_size': 741.3580222134027,
'step': 21.0,
'lr_policy': 'fixed',
'solver': '"adam"',
'gamma': 0.6788656698879015}, 0.040000000000000001),
({'num_layers': 4.0,
'power': 0.7949015212265006,
'solver': '"adadelta"',
'dropout_layer_1': 0.4521497425726368,
'dropout_output': 0.44804264005915573,
'dropout_layer_3': 0.5015510137781847,
'dropout_layer_2': 0.45385947764882123,
'num_units_layer_2': 5059.0,
'num_units_layer_3': 291.0,
'batch_size': 808.4213834239384,
'num_units_layer_1': 2468.0,
'step': 39.0,
'std_layer_1': 0.0014528596407236882,
'rho': 0.5198404112822554,
'num_epochs': 365.0,
'std_layer_3': 0.018883281420906187,
'std_layer_2': 0.060173127055786896,
'lr_policy': 'inv',
'learning_rate': 0.004445343676392594,
'gamma': 0.5816489765526873}, 0.02),
({'num_layers': 3.0,
'power': 0.951501985775478,
'solver': '"nesterov"',
'dropout_layer_1': 0.3653306482580645,
'dropout_output': 0.22582421055308477,
'learning_rate': 0.19202636600670817,
'dropout_layer_2': 0.21819303981244112,
'num_units_layer_2': 4648.0,
'batch_size': 254.02504767576468,
'num_units_layer_1': 5766.0,
'step': 76.0,
'momentum': 0.8768999623562101,
'num_epochs': 486.0,
'std_layer_2': 0.012236381842172813,
'lr_policy': 'fixed',
'std_layer_1': 0.012567729774967713,
'gamma': 0.6722873907885709}, 0.02),
({'num_layers': 3.0,
'power': 0.951501985775478,
'solver': '"adadelta"',
'dropout_layer_1': 0.3653306482580645,
'dropout_output': 0.3357527566089538,
'learning_rate': 0.19202636600670817,
'dropout_layer_2': 0.21819303981244112,
'num_units_layer_2': 6138.0,
'batch_size': 254.02504767576468,
'num_units_layer_1': 5766.0,
'step': 68.0,
'rho': 0.6568839756050975,
'num_epochs': 486.0,
'std_layer_2': 0.012236381842172813,
'lr_policy': 'fixed',
'std_layer_1': 0.012567729774967713,
'gamma': 0.6722873907885709}, 0.02),
({'num_layers': 3.0,
'power': 0.8196900527717096,
'solver': '"sgd"',
'dropout_layer_1': 0.2939609278437122,
'dropout_output': 0.2553598975807717,
'learning_rate': 0.0792359335703241,
'dropout_layer_2': 0.17148792984538072,
'num_units_layer_2': 6042.0,
'batch_size': 68.01704776734499,
'num_units_layer_1': 4337.0,
'step': 37.0,
'momentum': 0.7768588564816645,
'num_epochs': 467.0,
'std_layer_2': 0.09305184343463795,
'lr_policy': 'inv',
'std_layer_1': 0.0033240180436903202,
'gamma': 0.7240342867167526}, 0.040000000000000001),
({'num_layers': 3.0,
'power': 0.951501985775478,
'solver': '"nesterov"',
'dropout_layer_1': 0.7836649078292658,
'dropout_output': 0.21660605836335647,
'learning_rate': 0.027610542766288576,
'dropout_layer_2': 0.011586976261760496,
'num_units_layer_2': 5787.0,
'batch_size': 254.02504767576468,
'num_units_layer_1': 4722.0,
'step': 18.0,
'momentum': 0.8768999623562101,
'num_epochs': 486.0,
'std_layer_2': 0.012236381842172813,
'lr_policy': 'exp',
'std_layer_1': 0.0005598439981691678,
'gamma': 0.9667180658562067}, 0.040000000000000001),
({'num_layers': 3.0,
'power': 0.9747702817998765,
'solver': '"nesterov"',
'dropout_layer_1': 0.4051640090922492,
'dropout_output': 0.2892451691812821,
'learning_rate': 0.17394384345131372,
'dropout_layer_2': 0.5231272729308043,
'num_units_layer_2': 5608.0,
'batch_size': 73.2292167635178,
'num_units_layer_1': 4633.0,
'step': 16.0,
'momentum': 0.8358157637992942,
'num_epochs': 491.0,
'std_layer_2': 0.002570301444272789,
'lr_policy': 'step',
'std_layer_1': 0.00696310505976957,
'gamma': 0.1982457569106505}, 0.040000000000000001),
({'dropout_output': 0.25306857146792566,
'num_units_layer_4': 4733.0,
'num_epochs': 486.0,
'num_units_layer_2': 5797.0,
'num_units_layer_3': 5620.0,
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'num_epochs': 488.0,
'std_layer_3': 0.004455993940858878,
'std_layer_2': 0.07300638871271027,
'lr_policy': 'fixed',
'learning_rate': 0.1122446352398429,
'gamma': 0.7159714609304522}, 0.02),
({'num_layers': 4.0,
'power': 0.7038076018250163,
'solver': '"adadelta"',
'dropout_layer_1': 0.21049439968187753,
'dropout_output': 0.37339465970155605,
'dropout_layer_3': 0.4492220124816988,
'dropout_layer_2': 0.2556332708684139,
'num_units_layer_2': 2212.0,
'num_units_layer_3': 5926.0,
'batch_size': 934.4929652600218,
'num_units_layer_1': 1350.0,
'step': 23.0,
'std_layer_1': 0.00041345564765364986,
'rho': 0.6263095724704066,
'num_epochs': 57.0,
'std_layer_3': 0.058349495588107474,
'std_layer_2': 0.04610450347271679,
'lr_policy': 'fixed',
'learning_rate': 7.219811931684516e-05,
'gamma': 0.5372817819519713}, 0.02),
({'dropout_output': 0.05419711953824402,
'num_units_layer_4': 503.0,
'num_epochs': 249.0,
'num_units_layer_2': 1798.0,
'num_units_layer_3': 4435.0,
'num_units_layer_1': 5785.0,
'num_layers': 5.0,
'std_layer_3': 0.0020693367447187833,
'std_layer_2': 0.01626605159391926,
'std_layer_1': 0.02160469296878519,
'learning_rate': 0.161203081475512,
'std_layer_4': 0.04266796418763915,
'power': 0.7730778282076188,
'dropout_layer_1': 0.5996870640229259,
'dropout_layer_3': 0.2387361343390475,
'dropout_layer_2': 0.049116767959870425,
'dropout_layer_4': 0.6672322817450616,
'batch_size': 719.3714049032842,
'step': 14.0,
'lr_policy': 'fixed',
'solver': '"adagrad"',
'gamma': 0.10402671251350482}, 0.02),
({'dropout_output': 0.1570217658484297,
'num_units_layer_4': 5074.0,
'num_units_layer_5': 4907.0,
'num_units_layer_2': 5709.0,
'num_units_layer_3': 5344.0,
'num_units_layer_1': 2394.0,
'num_epochs': 439.0,
'std_layer_3': 0.07601832353856731,
'std_layer_2': 0.020438631464011356,
'lr_policy': 'step',
'learning_rate': 0.0010928685578229672,
'momentum': 0.7135023584881737,
'std_layer_4': 0.05067105296151777,
'power': 0.7904179532313478,
'dropout_layer_1': 0.407964865575413,
'dropout_layer_3': 0.1626118699682814,
'dropout_layer_2': 0.8417694944058159,
'dropout_layer_5': 0.5575846370595227,
'dropout_layer_4': 0.7007827393164742,
'batch_size': 702.817516948513,
'step': 3.0,
'std_layer_1': 0.011027585261841283,
'num_layers': 6.0,
'solver': '"nesterov"',
'std_layer_5': 0.0986334443081331,
'gamma': 0.41981866741792373}, 0.02)]
targets = []
predictions = []
predictions_valid = []
predictions_test = []
# Make predictions and weight them
iteration = 0
for config, weight in choices:
print(config)
print(weight)
# Extract layer specific variables
num_layers = int(config["num_layers"])
num_units_per_layer = []
dropout_per_layer = []
std_per_layer = []
for i in range(1, num_layers):
num_units_per_layer.append(int(config["num_units_layer_" + str(i)]))
dropout_per_layer.append(float(config["dropout_layer_" + str(i)]))
std_per_layer.append(float(config["std_layer_" + str(i)]))
# Extract solver specific variables
if config["solver"] == "adam":
beta1 = config["beta1"]
beta2 = config["beta2"]
else:
beta1 = None
beta2 = None
if config["solver"] == "adadelta":
rho = config["rho"]
else:
rho = None
if config["solver"] == "momentum" or config["solver"] == "nesterov":
momentum = config["momentum"]
else:
momentum = None
iteration += 1
n_feat = X_train.shape[1]
num_output_units = dm.info["label_num"]
input_shape = (int(config["batch_size"]), 1, 1, n_feat)
net = FeedForwardNet(input_shape=input_shape,
batch_size=config["batch_size"],
num_layers=num_layers,
num_units_per_layer=num_units_per_layer,
dropout_per_layer=dropout_per_layer,
std_per_layer=std_per_layer,