def initialize(self,
mlsl,
nnl,
seed=None,
weight_range=1.0,
outputs_from_mlsl=None,
use_softmax=True):
"""
Initialize an object of this class that binds a new NN on top
of an existing MLSL object
:param mlsl:
:type mlsl: MLSL
:param nnl:
:type nnl: list
:param seed:
:type seed:
:param weight_range:
:type weight_range:
:return:
:rtype:
"""
self.mlsl_output_size = mlsl.output_sizes[
-1] if outputs_from_mlsl else outputs_from_mlsl
# Change input size of Neural net to assigned feature size plus MLSL outputs
nnl[0] += self.mlsl_output_size
self.outputs_from_mlsl = outputs_from_mlsl
self.mlsl = mlsl
self.nnet = DNN()
self.nnet.initialize(nnl=nnl, seed=seed, weight_range=weight_range)
self.use_softmax = use_softmax
def evaluate_mlp_ner():
cws = DNN('mlp', mode=TrainMode.Sentence, is_seg=True, task='ner')
model = 'tmp/mlp/mlp-ner-model1.ckpt'
# print(cws.seg('在中国致公党第十一次全国代表大会隆重召开之际，中国共产党中央委员会谨向大会表示热烈的祝贺，向致公党的同志们', model,ner=True))
print(cws.seg('多饮多尿多食', model, ner=True))
print(cws.seg('无明显小便泡沫增多,伴有夜尿3次。', model, ner=True))
print(cws.seg('无明显双脚疼痛,无间歇性后跛行,无明显足部红肿破溃', model, ner=True, debug=False))
def get_ner(content, model_name):
if model_name.startswith('tmp/mlp'):
dnn = DNN('mlp', mode=TrainMode.Sentence, task='ner', is_seg=True)
else:
dnn = DNN('lstm', task='ner', is_seg=True)
ner = dnn.seg(content, model_path=model_name, ner=True, trans=True)
return ner[1]
def __init__(self, num_actions, observation_shape, dqn_params, cnn_params, folder):
self.num_actions = num_actions
self.observation_shape= observation_shape
self.cnn_params = cnn_params
self.folder = folder
self.epsilon = dqn_params['epsilon']
self.gamma = dqn_params['gamma']
self.mini_batch_size = dqn_params['mini_batch_size']
self.time_step = 0
self.decay_rate = dqn_params['decay_rate']
self.epsilon_min = dqn_params['epsilon_min']
self.current_epsilon = self.epsilon
self.use_ddqn = dqn_params['use_ddqn']
self.print_obs = dqn_params['print_obs']
self.print_reward = dqn_params['print_reward']
self.startTraining = False
#memory for printing reward and observations
self.memory = deque(maxlen=1000)
#PER memory
self.per_memory = Memory(dqn_params['memory_capacity'])
#initialize network
self.model = DNN(folder, num_actions, observation_shape, cnn_params)
print("model initialized")
#extra network for Double DQN
if self.use_ddqn == 1:
self.target_model = CNN(folder, num_actions, observation_shape, cnn_params)
def main():
x_train, y_train, x_test, y_test = data.mnist(one_hot=True)
# Define Deep Neural Network structure (input_dim, num_of_nodes)
layers = [[x_train.shape[1], 256], [256, 128], [128, 64]]
# Initialize a deep neural network
dnn = DNN(MODEL_FOLDER, os_slash, layers, params)
pre_epochs = 100
train_epochs = 100
# Create auto-encoders and train them one by one by stacking them in the DNN
pre_trained_weights = dnn.pre_train(x_train, pre_epochs)
# Then use the pre-trained weights of these layers as initial weight values for the MLP
history = dnn.train(x_train,
y_train,
train_epochs,
init_weights=pre_trained_weights)
plot.plot_loss(history, loss_type='MSE')
predicted, score = dnn.test(x_test, y_test)
print("Test accuracy: ", score[1])
dnn.model.save_weights(MODEL_FOLDER + os_slash + "final_weights.h5")
dnn.model.save(MODEL_FOLDER + os_slash + "model.h5")
save_results(score[1])
def __init__(self,
numpy_rng,
theano_rng=None,
cfg=None,
cfg_tower1=None,
cfg_tower2=None):
self.layers = []
self.params = []
self.delta_params = []
self.cfg = cfg
self.cfg_tower1 = cfg_tower1
self.cfg_tower2 = cfg_tower2
if not theano_rng:
theano_rng = RandomStreams(numpy_rng.randint(2**30))
# allocate symbolic variables for the data
self.x = T.matrix('x')
self.y = T.ivector('y')
self.input_tower1 = self.x[:, 0:cfg_tower1.n_ins]
self.input_tower2 = self.x[:, cfg_tower1.n_ins:(cfg_tower1.n_ins +
cfg_tower2.n_ins)]
self.dnn_tower1 = DNN(numpy_rng=numpy_rng,
theano_rng=theano_rng,
cfg=self.cfg_tower1,
input=self.input_tower1)
self.dnn_tower2 = DNN(numpy_rng=numpy_rng,
theano_rng=theano_rng,
cfg=self.cfg_tower2,
input=self.input_tower2)
concat_output = T.concatenate([
self.dnn_tower1.layers[-1].output,
self.dnn_tower2.layers[-1].output
],
axis=1)
self.dnn = DNN(numpy_rng=numpy_rng,
theano_rng=theano_rng,
cfg=self.cfg,
input=concat_output)
self.layers.extend(self.dnn_tower1.layers)
self.params.extend(self.dnn_tower1.params)
self.delta_params.extend(self.dnn_tower1.delta_params)
self.layers.extend(self.dnn_tower2.layers)
self.params.extend(self.dnn_tower2.params)
self.delta_params.extend(self.dnn_tower2.delta_params)
self.layers.extend(self.dnn.layers)
self.params.extend(self.dnn.params)
self.delta_params.extend(self.dnn.delta_params)
self.finetune_cost = self.dnn.logLayer.negative_log_likelihood(self.y)
self.errors = self.dnn.logLayer.errors(self.y)
def runDNNTrain(self, corpus=None, learningRate=0.02, hiddenUnitSize=[128, 256, 128], dataKey='Question', labelKey='y'):
if corpus is None:
corpus = self.prepareDF(excelLocation=TRAIN_EXCEL)
self.dnnObject = DNN(pd_df_train=corpus,
pd_df_test=None,
learning_rate=learningRate,
hidden_units_size=hiddenUnitSize,
dataKey=dataKey,
labelKey=labelKey)
result = self.dnnObject.run()
return result
def create_columns(self, columns=5):
for w in self.Ws:
x_train = self.train_datasets[w]
self.dnns[w] = []
for i in range(columns):
model = DNN(width=x_train.shape[1],
height=x_train.shape[2],
depth=x_train.shape[3],
classes=10)
model.compile(loss="categorical_crossentropy",
optimizer=Adadelta(),
metrics=["accuracy"])
self.dnns[w].append(model)
def __init__(self,
numpy_rng,
theano_rng=None,
cfg_si=None,
cfg_adapt=None):
# allocate symbolic variables for the data
self.x = T.matrix('x')
self.y = T.ivector('y')
# we assume that i-vectors are appended to speech features in a frame-wise manner
self.feat_dim = cfg_si.n_ins
self.ivec_dim = cfg_adapt.n_ins
self.iv = self.x[:, self.feat_dim:self.feat_dim + self.ivec_dim]
self.feat = self.x[:, 0:self.feat_dim]
# the parameters
self.params = [] # the params to be updated in the current training
self.delta_params = []
# the i-vector network
dnn_adapt = DNN(numpy_rng=numpy_rng,
theano_rng=theano_rng,
cfg=cfg_adapt,
input=self.iv)
self.dnn_adapt = dnn_adapt
# the final output layer which has the same dimension as the input features
linear_func = lambda x: x
adapt_output_layer = HiddenLayer(
rng=numpy_rng,
input=dnn_adapt.layers[-1].output,
n_in=cfg_adapt.hidden_layers_sizes[-1],
n_out=self.feat_dim,
activation=linear_func)
dnn_adapt.layers.append(adapt_output_layer)
dnn_adapt.params.extend(adapt_output_layer.params)
dnn_adapt.delta_params.extend(adapt_output_layer.delta_params)
dnn_si = DNN(numpy_rng=numpy_rng,
theano_rng=theano_rng,
cfg=cfg_si,
input=self.feat + dnn_adapt.layers[-1].output)
self.dnn_si = dnn_si
# construct a function that implements one step of finetunining
# compute the cost for second phase of training,
# defined as the negative log likelihood
self.finetune_cost = dnn_si.logLayer.negative_log_likelihood(self.y)
self.errors = dnn_si.logLayer.errors(self.y)
def runDNNTrain(self,
learningRate=0.01,
hiddenUnitSize=[100, 100],
dataKey='Question',
labelKey='y'):
corpusTrain = self.prepareDF(excelLocation=TRAIN_EXCEL)
dnnObject = DNN(pd_df_train=corpusTrain,
pd_df_test=None,
learning_rate=learningRate,
hidden_units_size=hiddenUnitSize,
dataKey=dataKey,
labelKey=labelKey)
result = dnnObject.run()
return result
def evaluate_lstm():
cws = DNN('lstm', is_seg=True)
model = 'tmp/lstm-model100.ckpt'
print(cws.seg('小明来自南京师范大学', model, debug=True))
print(cws.seg('小明是上海理工大学的学生', model))
print(cws.seg('迈向充满希望的新世纪', model))
print(cws.seg('我爱北京天安门', model))
print(cws.seg('多饮多尿多食', model))
print(cws.seg('无明显小便泡沫增多,伴有夜尿3次。无明显双脚疼痛,无间歇性后跛行,无明显足部红肿破溃', model))
def main2():
dnn = DNN(input=28 * 28,
layers=[DropoutLayer(160, LQ),
Layer(10, LCE)],
eta=0.05,
lmbda=1) # 98%
dnn.initialize_rand()
train, test, vadilation = load_mnist_simple()
f_names = [f'mnist_expaned_k0{i}.pkl.gz' for i in range(50)]
shuffle(f_names)
for f_name in f_names:
print(f_name)
with timing("load"):
raw_data = load_data(f_name)
with timing("shuffle"):
shuffle(raw_data)
with timing("reshape"):
data = [(x.reshape((784, 1)), y)
for x, y in islice(raw_data, 100000)]
del raw_data
with timing("learn"):
dnn.learn(data)
del data
print('TEST:', dnn.test(test))
def __init__(self, numpy_rng, theano_rng=None, cfg=None, testing=False):
self.layers = []
self.params = []
self.delta_params = []
self.conv_layers = []
self.cfg = cfg
self.conv_layer_configs = cfg.conv_layer_configs
self.conv_activation = cfg.conv_activation
self.use_fast = cfg.use_fast
if not theano_rng:
theano_rng = RandomStreams(numpy_rng.randint(2**30))
# allocate symbolic variables for the data
self.x = T.matrix('x')
self.y = T.ivector('y')
self.conv_layer_num = len(self.conv_layer_configs)
for i in xrange(self.conv_layer_num):
if i == 0:
input = self.x
else:
input = self.layers[-1].output
config = self.conv_layer_configs[i]
conv_layer = ConvLayer(numpy_rng=numpy_rng,
input=input,
input_shape=config['input_shape'],
filter_shape=config['filter_shape'],
poolsize=config['poolsize'],
activation=self.conv_activation,
flatten=config['flatten'],
use_fast=self.use_fast,
testing=testing)
self.layers.append(conv_layer)
self.conv_layers.append(conv_layer)
self.params.extend(conv_layer.params)
self.delta_params.extend(conv_layer.delta_params)
self.conv_output_dim = config['output_shape'][1] * config[
'output_shape'][2] * config['output_shape'][3]
cfg.n_ins = config['output_shape'][1] * config['output_shape'][
2] * config['output_shape'][3]
self.fc_dnn = DNN(numpy_rng=numpy_rng,
theano_rng=theano_rng,
cfg=self.cfg,
input=self.layers[-1].output)
self.layers.extend(self.fc_dnn.layers)
self.params.extend(self.fc_dnn.params)
self.delta_params.extend(self.fc_dnn.delta_params)
self.finetune_cost = self.fc_dnn.logLayer.negative_log_likelihood(
self.y)
self.errors = self.fc_dnn.logLayer.errors(self.y)
def __init__(self, name='Goodone', net=None, train=0):
"""
初始化
net: 训练的神经网络
verity：使用还是验证阶段
验证阶段，神经网络未训练
使用阶段，神经网络已训练
"""
self.env = gym.make("MountainCarContinuous-v0")
self.name = name
self.simulation_step = 0.1
self.units = 50
self.ratio = 200
self.reset()
if net:
self.net = net
else:
self.net = DNN(1, 1, self.units, train=train, name=self.name)
def getDNN(loader, args):
sys.path.insert(1, os.getcwd())
#try:
from dnn import DNN
# if args.rank == 0:
# print('Successfully imported your model.')
# except:
# if args.rank == 0:
# print('Could not import your model. Exiting.')
# exit(-1)
return DNN(loader.getXShape())
def __init__(self, nnet_conf, input_dim, output_dim, feature_conf, num_gpus = 1,
use_gpu = True, gpu_ids = '-1', summary_dir = None):
''' just some basic config for this trainer '''
self.arch = nnet_conf['nnet_arch']
#tensorflow related
self.graph = None
self.sess = None
#feature related
self.batch_size = feature_conf['batch_size']
self.max_length = feature_conf.get('max_length', 0)
self.jitter_window = feature_conf.get('jitter_window', 0)
#nnet training & decoding
self.buckets_tr = nnet_conf.get('buckets_tr', None)
self.buckets = nnet_conf.get('buckets', None)
# for learning rate schedule. None in default (means scheduler outside)
# otherwise use prep_learning_rate
self.global_step = None
self.learning_rate = None
#gpu related
self.wait_gpu = True
self.num_gpus = num_gpus
self.use_gpu = use_gpu
self.gpu_ids = gpu_ids
#summary directory
self.summary_dir = summary_dir
if self.arch == 'dnn':
self.model = DNN(input_dim, output_dim, self.batch_size, num_gpus)
elif self.arch == 'bn':
self.model = BN(input_dim, output_dim, self.batch_size, num_gpus)
elif self.arch == 'lstm':
self.model = LSTM(input_dim, output_dim, self.batch_size, self.max_length, num_gpus)
elif self.arch == 'seq2class':
self.model = SEQ2CLASS(input_dim, output_dim, self.batch_size, self.max_length, num_gpus,
buckets_tr = self.buckets_tr, buckets = self.buckets)
elif self.arch == 'jointdnn':
self.model = JOINTDNN(input_dim, output_dim, self.batch_size, self.max_length, num_gpus,
buckets_tr = self.buckets_tr, buckets = self.buckets,
mode = nnet_conf.get('mode', 'joint'))
elif self.arch == 'jointdnn-sid':
self.model = JOINTDNN(input_dim, output_dim, self.batch_size, self.max_length, num_gpus,
buckets_tr = self.buckets_tr, buckets = self.buckets, mode = 'sid')
elif self.arch == 'jointdnn-asr':
self.model = JOINTDNN(input_dim, output_dim, self.batch_size, self.max_length, num_gpus,
buckets_tr = self.buckets_tr, buckets = self.buckets, mode = 'asr')
else:
raise RuntimeError("arch type %s not supported", self.arch)
def main():
train, test, vadilation = load_mnist_simple()
# x, y = train[0]
# print("x: ", x.shape)
# print("y: ", y)
with timing(f""):
# dnn = DNN(input=28 * 28, layers=[Layer(30, LQ), Layer(10, LCE)], eta=0.05) # 96%
# dnn = DNN(input=28 * 28, layers=[Layer(30, LQ), Layer(10, SM)], eta=0.001) # 68%
# dnn = DNN(input=28 * 28, layers=[Layer(100, LQ), Layer(10, LCE)], eta=0.05, lmbda=5) # 98%
# dnn = DNN(input=28 * 28, layers=[DropoutLayer(100, LQ), Layer(10, LCE)], eta=0.05) # 97.5%
dnn = DNN(input=28 * 28, layers=[DropoutLayer(160, LQ), Layer(10, LCE)], eta=0.05, lmbda=3)
dnn.initialize_rand()
dnn.learn(train, epochs=30, test=vadilation, batch_size=29)
print('test:', dnn.test(test))
print(dnn.stats())
def run_experiment(hparams):
'''
Google ML Engine entry point for training job.
'''
model = DNN(784, 10)
init = tf.global_variables_initializer()
with tf.Session() as session:
tf.nn.in_top_k(tf.constant([[2.0, 9.0], [7.0, 5.0], [0.0, 0.0]]),
tf.constant([1, 0, 1]), 1).eval()
session.run(init)
for epoch in range(0, hparams.epochs):
for X, Y in read_file_in_batches(hparams.train_file):
session.run(
model.training,
feed_dict={
model.x: X,
model.y: Y
},
)
X, Y = get_eval_data(hparams.eval_file)
print("Epoch(%s) Error: %s " %
(epoch,
session.run(model.error, feed_dict={
model.x: X,
model.y: Y
})))
X, Y = get_eval_data(hparams.eval_file)
for i in range(10):
print("==================================")
plt.imshow(np.array(X[i]).reshape([28, 28]))
plt.show()
print("::: %s" % Y[i])
result = session.run(model.logits, feed_dict={model.x: [
X[i],
]})
print("::: %s" % result)
print("::: Digit: %s" %
session.run(tf.argmax(result.reshape(10), 0)))
def evaluate_mlp():
cws = DNN('mlp', mode=TrainMode.Sentence)
model = 'tmp/mlp-model20.ckpt'
# print(cws.seg('小明来自南京师范大学', model, debug=True))
# print(cws.seg('小明是上海理工大学的学生', model))
# print(cws.seg('迈向充满希望的新世纪', model))
# print(cws.seg('我爱北京天安门', model))
# print(cws.seg('在中国致公党第十一次全国代表大会隆重召开之际，中国共产党中央委员会谨向大会表示热烈的祝贺，向致公党的同志们',model))
print(cws.seg('多饮多尿多食', model))
print(cws.seg('无明显小便泡沫增多,伴有夜尿3次。', model))
print(cws.seg('无明显小便泡沫增多,伴有夜尿3次。', model, ner=True))
print(cws.seg('无明显双脚疼痛,无间歇性后跛行,无明显足部红肿破溃', model))
def plot_accuracy():
import numpy as np
from matplotlib import pyplot as plt
train, test, vadilation = load_mnist_simple()
dnn = DNN(input=28 * 28, layers=[Layer(100, LQ), Layer(10, LCE)], eta=0.05, lmbda=1)
for l in dnn.layers:
l.w = np.random.random(l.w.shape) - 0.5
acc1 = list(dnn.learn_iter(train, epochs=20, test=vadilation))
dnn.initialize_rand()
acc2 = list(dnn.learn_iter(train, epochs=20, test=vadilation))
print(acc1)
print(acc2)
plt.plot(acc1)
plt.plot(acc2)
plt.show()
def main():
options = {
'learning_rate': 0.1,
'beta1': 0.9,
'optimizer': 'gd',
'loss': 'crossentropy'
}
train_x, test_x, train_set_x_orig, train_set_y_orig, test_set_x_orig, test_set_y_orig, classes = load_data(
)
X = np.array([[1, 2], [1, 2], [4, 2]])
Y = np.array([[0], [0], [0]])
print(train_x.shape)
print(test_x.shape)
print(train_set_y_orig.shape)
print(train_set_y_orig[0, 0:10])
layers = [
Dense(32, activation='relu'),
Dense(5, activation='relu'),
Dense(1, activation='sigmoid')
]
print(len(layers))
dnn = DNN(train_x, train_set_y_orig, layers, options)
print(dnn.params.keys())
#for param in sorted(dnn.params):
# print(param, dnn.params[param].shape)
print(dnn)
print(dnn.loss(dnn.predict(test_x), test_set_y_orig))
dnn.train()
return float(sum(array))/len(array)
boston = datasets.load_boston()
#boston = datasets.make_regression()
#data = boston[0]
#target = boston[1]
data = boston.data
target = boston.target
matrix = Preprocess.to_matrix(list(data))
matrix = Preprocess.scale(matrix)
matrix = list(matrix)
target = list(target)
layers = [13,7,1]
dnn = DNN(matrix, target, layers, hidden_layer="TanhLayer", final_layer="LinearLayer", compression_epochs=5, smoothing_epochs=0, bias=True)
full = dnn.fit()
print full
#preds = [dnn.predict(d)[0] for d in matrix]
preds = [full.activate(d)[0] for d in matrix]
print "mrse preds {0}".format(mrse(preds, target))
print "rmse preds {0}".format(rmse(preds, target))
#mean = avg(target)
#mean = [mean for i in range(len(target))]
#print "mrse mean {0}".format(mrse(mean, target))
#print "rmse mean {0}".format(rmse(mean, target))
#for i in range(10):
# d = matrix[i]
maxseq_length = 100
embedding_size = 300
batch_size = 32
keep_prob = 1.0
test_data = read_data('data/test.txt')
test_data = np.array(test_data)
test_X = test_data[:,0]
test_Y = test_data[:,[-1]]
word2vec = word2vec_load()
if model_type == 'logistic':
model = Logistic(maxseq_length, embedding_size)
elif model_type == 'dnn':
model = DNN(maxseq_length, embedding_size)
elif model_type == 'rnn':
model = RNN(batch_size, maxseq_length, embedding_size)
elif model_type == 'lstm':
model = LSTM(batch_size, maxseq_length, embedding_size, keep_prob)
elif model_type == 'cnn':
model = CNN(batch_size, maxseq_length, embedding_size)
with tf.Session() as sess:
total_batch = int(len(test_X) / batch_size)
save_path = './saved/' + model_type + '/model-' + str(test_epoch)
sess.run(tf.global_variables_initializer())
saver = tf.train.Saver()
saver.restore(sess, save_path)
def main():
print('=========================================')
print(' Numpy DNN ')
print(' 26/Nov/2017 ')
print(' By Thang Vu ([email protected]) ')
print('=========================================')
# load datasets
path = 'data/mnist.pkl.gz'
train_set, val_set, test_set = load_mnist_datasets(path)
batch_size = 128
X_train, y_train = train_set
X_val, y_val = val_set
X_test, y_test = test_set
# bookeeping for best model based on validation set
best_val_acc = -1
best_model = None
# create model and optimization method
dnn = DNN()
sgd = SGD(lr=0.1, lr_decay=0.1, weight_decay=1e-3, momentum=0.9)
# Train
batch_size = 128
for epoch in range(20):
dnn.train_mode() # set model to train mode (because of dropout)
num_train = X_train.shape[0]
num_batch = num_train//batch_size
for batch in range(num_batch):
# get batch data
batch_mask = np.random.choice(num_train, batch_size)
X_batch = X_train[batch_mask]
y_batch = y_train[batch_mask]
# forward
output = dnn.forward(X_batch)
loss, dout = softmax_cross_entropy_loss(output, y_batch)
if batch%100 == 0:
print("Epoch %2d Iter %3d Loss %.5f" %(epoch, batch, loss))
# backward and update
grads = dnn.backward(dout)
sgd.step(dnn.params, grads)
sgd.decay_learning_rate() # decay learning rate after one epoch
dnn.eval_mode() # set model to eval mode
train_acc = check_acc(dnn, X_train, y_train)
val_acc = check_acc(dnn, X_val, y_val)
if(best_val_acc < val_acc):
best_val_acc = val_acc
best_model = dnn
# store best model based n acc_val
print('Epoch finish. ')
print('Train acc %.3f' %train_acc)
print('Val acc %.3f' %val_acc)
print('-'*30)
print('')
print('Train finished. Best acc %.3f' %best_val_acc)
test_acc = check_acc(best_model, X_test, y_test)
print('Test acc %.3f' %test_acc)
import matplotlib.pyplot as plt
from aircraft_obj import AircraftEnv
from dnn import DNN
from verticalguidence import guidance
from scipy.optimize import root
from scipy.interpolate import interp1d
import math
# 横纵向弹道 横向确定方向，纵向确定大小
if __name__ == '__main__':
# 纵向网络的定义
num = 7000
net = DNN(2,
1,
512,
train=0,
isnorm=True,
name='w_%s' % str(num),
scale=[0.1, 100, 100]) # 定义网络
# 定义训练对象
cav = AircraftEnv()
# 进行飞行
info = guidance(cav, net, tht_direction='strategy')
states, ws, hcmds, thts = info['state_records'], info['w_records'], info[
'hcmd_records'], info['tht_records']
ts, angles = info['ts'], info['angles']
# 纵向制导画图
# 单个HV走廊
fig1, ax1 = plt.subplots()
cav.plot(states, hcmds, ax=ax1)
ax1.set_xlabel('V(m/s)')
'target_error': cav.calculate_range(),
'angles': np.array(angles),
'ts': t
}
return info
if __name__ == '__main__':
train_mode = 0 # 是否进行网络训练,0不训练，1从0开始训练，2从之前基础上开始训练
num = 7000
g = tf.Graph()
memory = np.load('Trajectories/memory_%s.npy' % num) # 读取数据
net = DNN(2,
1,
512,
train=train_mode,
isnorm=True,
name='w_%s' % str(num),
graph=g,
scale=[0.1, 100, 100]) # 定义网络
memory_norm = net.norm(memory)
if train_mode != 0:
# 训练模式
X = memory_norm[:, 1:].copy()
Y = memory_norm[:, 0:1].copy()
losses = []
for i in range(5000):
sample_index = np.random.choice(len(X), size=1000)
batch_x = X[sample_index, :]
batch_y = Y[sample_index, :]
loss, mae = net.learn(batch_x, batch_y)
losses.append(loss)
def init_DNN(p, hidden_layers_units, nbr_classes):
""" construit et d’initialise les poids et les biais d’un DNN"""
return DNN(p, hidden_layers_units, nbr_classes)
class MountainCar:
"""
定义预测出来的模型
"""
def __init__(self, name='Goodone', net=None, train=0):
"""
初始化
net: 训练的神经网络
verity：使用还是验证阶段
验证阶段，神经网络未训练
使用阶段，神经网络已训练
"""
self.env = gym.make("MountainCarContinuous-v0")
self.name = name
self.simulation_step = 0.1
self.units = 50
self.ratio = 200
self.reset()
if net:
self.net = net
else:
self.net = DNN(1, 1, self.units, train=train, name=self.name)
def save_samples(self, big_epis=100):
"""
保存运行得到的数据
得到的数据有big_epis*3000行
"""
record = []
for big_epi in range(big_epis):
# 初始化
# 为了能够达到目标点
a = 0.0025
change = 100
observation = self.reset()
for epi in range(10000):
if epi % change == 0:
u = self.action_sample() * 3
print(big_epi, int(20 * epi / 3000) * '=')
observation_old = observation.copy()
observation, _, done, _ = self.env.step(u)
target = self._get_target(observation_old, observation, u)
x = observation_old[0]
# 保存真实值和计算得到的值，后期作为比较
# record.append([x, target, -a * math.cos(3 * x)])
record.append([x, target])
data = np.array(record)
np.save(os.path.join(self.net.model_path0, 'memory.npy'), data)
return data
def verity_data(self):
"""
验证数据集的正确性，画出两个自己计算出来的值和真实值的区别
"""
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
sns.set()
self.data = self._load_data()
data_size = len(self.data)
indexs = np.random.choice(data_size, size=int(data_size / 10))
df = pd.DataFrame(self.data[indexs, :],
columns=['position', 'target_dot', 'real_dot'])
plt.figure()
plt.scatter(df['position'],
df['target_dot'] * 1.1,
s=5,
label='target') # 为了显示出区别乘以1.1
plt.scatter(df['position'], df['real_dot'], s=5, label='real')
plt.legend()
plt.show()
def train_model(self):
"""
利用得到的数据对模型进行训练，首先对数据进行缩放，之后利用神经网络进行拟合
"""
# 训练
data = self._load_data()
data[:, 1:] = data[:, 1:] * self.ratio
self.net.learn_data(data)
self.net.store_net()
def verity_net_1(self):
"""
验证神经网络的正确性
"""
a = 0.0025
x_ = np.arange(-1.1, 0.5, 0.001)
y_tru = -a * np.cos(3 * x_)
y_pre = self.net.predict(x_.reshape((-1, 1))) / self.ratio
# 验证对所有的x的拟合情况
fig = plt.figure()
plt.plot(x_, y_tru, label='x_tru')
plt.plot(x_, y_pre, label='x_pre')
plt.legend()
y_tru_dot = 3 * a * np.sin(3 * x_)
y_pre_dot = self.net.predict_dot(x_.reshape(
(-1, 1)))[:, 0] / self.ratio
# y_pre_dot = self.net.predict_dot(x_.reshape((-1, 1)))[:, 0]
# 验证对所有的x_dot的拟合情况
fig = plt.figure()
plt.plot(x_, y_tru_dot, label='x_dot_tru')
plt.plot(x_, y_pre_dot, label='x_dot_pre')
plt.legend()
plt.show()
def verity_net_2(self):
"""
验证神经网络的正确性2
与真实系统的的比较
"""
observation_record = []
observation_record_net = []
time_record = []
observation = self.reset()
observation_net = observation
change = 100
time = 0
epi = 0
while True:
observation_record.append(observation)
observation_record_net.append(observation_net)
time_record.append(time)
if epi % change == 0:
action = self.action_sample() * 3
epi += 1
observation, _, done, info = self.env.step(action)
observation_net, _, done_net, info_net = self.step(action)
time += self.simulation_step
print(observation, observation_net)
if done_net:
break
observation_record = np.array(observation_record)
observation_record_net = np.array(observation_record_net)
time_record = np.array(time_record)
plt.figure(1)
plt.plot(time_record, observation_record[:, 0], label='x_ture')
plt.plot(time_record, observation_record_net[:, 0], label='x_pre')
plt.xlabel('Time(s)')
plt.ylabel('Xposition')
plt.plot(time_record, 0.45 * np.ones(len(observation_record)), 'r')
plt.legend()
plt.figure(2)
plt.plot(time_record, observation_record[:, 1], label='v_ture')
plt.plot(time_record, observation_record_net[:, 1], label='v_pre')
plt.xlabel('Time(s)')
plt.ylabel('Vspeed')
plt.legend()
plt.show()
def _load_data(self):
"""
将最开始得到的数据读取出来
:return:
"""
data = np.load(os.path.join(self.net.model_path0, 'memory.npy'))
return data
def action_sample(self):
"""
随机选取符合环境的动作
"""
return self.env.action_space.sample()
def reset(self):
"""
利用原始问题的初始化，随机初始化
"""
self.state = self.env.reset()
return self.state
def step(self, action):
"""
利用神经网络进行模型辨识
"""
action = min(max(action, -1.0), 1.0)
x, v = self.state
# 神经网络得到的导数
dot = self.get_dot(self.state)
v_dot = 0.0015 * action + dot[0]
v = v + v_dot * self.simulation_step
v = min(max(v, -0.07), 0.07)
# 通过v计算x
x = x + self.simulation_step * v
x = min(max(x, -1.2), 0.6)
X = np.array([x, v])
if X.ndim == 2:
X = X.reshape((2, ))
self.state = X
# 返回参数
info = {}
done = {}
reward = {}
if x >= 0.45:
done = True
return self.state, reward, done, info
def step_true(self, action):
"""
利用原进行模型辨识
"""
action = min(max(action, -1.0), 1.0)
x, v = self.state
# 神经网络得到的导数
# dot = self.get_dot(self.state)
v_dot = 0.0015 * action - 0.0025 * math.cos(3 * x)
v = v + v_dot * self.simulation_step
v = min(max(v, -0.07), 0.07)
# 通过v计算x
x = x + self.simulation_step * v
x = min(max(x, -1.2), 0.6)
X = np.array([x, v])
if X.ndim == 2:
X = X.reshape((2, ))
self.state = X
# 返回参数
info = {}
done = {}
reward = {}
if x >= 0.45:
done = True
return self.state, reward, done, info
def get_dot(self, X):
return self.net.predict(X[0:1])[0] / self.ratio
def get_dot2(self, X):
return self.net.predict_dot(X[0:1])[0] / self.ratio
def _get_target(self, X, X_new, u):
"""
得到神经网络需要的真实值
首先求真实的导数，之后计算真实值
"""
u = min(max(u, -1.0), 1.0)
return (((X_new - X) / self.simulation_step)[1] - u * 0.0015)
elif sys.argv[1] == 'lr':
from lr import LR
model = LR(user_count=user_count,
item_count=item_count,
cate_count=cate_count,
cate_list=cate_list)
elif sys.argv[1] == 'lrcross':
from lrcross import LR
model = LR(user_count=user_count,
item_count=item_count,
cate_count=cate_count,
cate_list=cate_list)
elif sys.argv[1] == 'dnn':
from dnn import DNN
model = DNN(user_count=user_count,
item_count=item_count,
cate_count=cate_count,
cate_list=cate_list)
elif sys.argv[1] == 'widedeep':
from widedeep import WideDeep
model = WideDeep(user_count=user_count,
item_count=item_count,
cate_count=cate_count,
cate_list=cate_list)
else:
print('usage train.py lr|dnn|widedeep|deepfm')
sys.exit(1)
with tf.Session() as sess:
## TODO 初始化局部和全局变量
raise NotImplementedError()
class TrainTieBot:
def __init__(self):
self.bagOfWords = None
self.wordFeatures = None
def tokenize(self, corpus):
if not isinstance(corpus, dict):
raise(TrainTieBotException('Corpus must be of type:dict()'))
# # tokenize each sentence
tokenizer = RegexpTokenizer(r'\w+')
token = [str(x) for x in tokenizer.tokenize(corpus.values()[0].lower())]
return token
def cleanUpQuery(self, sentence):
tokenizer = RegexpTokenizer(r'\w+')
sentence = sentence.lower()
return tokenizer.tokenize(sentence)
def getFeaturesByName(self, corpus):
words = list()
if not isinstance(corpus, list):
raise(TrainTieBotException('Corpus must be of type:list()'))
for x in corpus:
words.extend(x.values()[0])
self.wordFeatures = list(set(words))
return self.wordFeatures
def BOW(self, corpus):
if not isinstance(corpus, list):
raise(TrainTieBotException('Corpus must be of type:list()'))
tokenDict = [{x.keys()[0]: self.tokenize(x)} for x in corpus]
getWordFeatures = self.getFeaturesByName(tokenDict)
vectorDataFrame = pd.DataFrame(data=np.zeros([len(corpus), len(getWordFeatures)]).astype(int))
# add a label column
labels = [x.keys()[0] for x in corpus]
for word in vectorDataFrame.index.tolist():
vectorDataFrame.loc[word, :] = \
[corpus[word].values()[0].count(item) if item in corpus[word].values()[0] else 0 for item in getWordFeatures]
vectorDataFrame['y'] = labels
self.bagOfWords = vectorDataFrame
return self.bagOfWords
def BOWFit(self, query):
if self.bagOfWords is None:
raise (TrainTieBotException('Create Bag of Words vectors before fitting it to a new query.'))
tokenDict = [{x.keys()[0]: self.tokenize(x)} for x in query]
arrayFitDf = pd.DataFrame(data=np.zeros([len(tokenDict), len(self.wordFeatures)])).astype(int)
arrayFitDf['y'] = [x.keys()[0] for x in query]
for i in range(len(query)):
arrayFitDf.iloc[i, :-1] = [query[i].values()[0].count(item) if item in query[i].values()[0] else 0 for item in self.wordFeatures]
return arrayFitDf
def list2df(self, data, dataKey, labelKey):
df = {}
df[dataKey] = []
df[labelKey] = []
for obj in data:
df[dataKey].append(obj.values()[0])
df[labelKey].append(obj.keys()[0])
return pd.DataFrame.from_dict(df)
def df2list(self, df, dataKey, labelKey):
df = df[[dataKey, labelKey]]
return [{df[labelKey][x]:df[dataKey][x]} for x in range(df.shape[0])]
def prepareDF(self, corpus=None, dataKey='Question', labelKey='y', excelLocation=None):
if corpus is None:
corpusObj = Corpus()
corpusT = corpusObj.loadData(excelLocation)
corpusT = self.df2list(corpusT, dataKey, labelKey)
# Expand Vocabulary list with part of speeches
corpusT = corpusObj.getExpandedSentences(corpusT)
# #print "corpus test:"
corpusT = self.list2df(corpusT, dataKey, labelKey)
else:
corpusObj = Corpus()
corpusT = corpusObj.getExpandedSentences(corpus)
corpusT = self.list2df(corpusT, dataKey, labelKey)
return corpusT
def getX(self, corpus=None):
dataKey = 'Question'
labelKey = 'y'
corpusObj = Corpus()
if corpus is None:
corpusTrain = corpusObj.loadData(TRAIN_EXCEL)
corpusTrain = self.df2list(corpusTrain, dataKey, labelKey)
corpusTrain = corpusObj.getExpandedSentences(corpusTrain)
BOWTrain = self.BOW(corpusTrain)
X = BOWTrain.iloc[:, :-1]
y = BOWTrain.iloc[:, -1]
else:
corpusTrain = corpusObj.getExpandedSentences(corpus)
BOWTrain = self.BOW(corpusTrain)
X = BOWTrain.iloc[:, :-1]
y = BOWTrain.iloc[:, -1]
return X, y
def gety(self, query):
dataKey = 'Question'
labelKey = 'y'
corpusTestObj = Corpus()
corpusTest = corpusTestObj.getExpandedSentences(query)
BOWTest = self.BOWFit(corpusTest)
X_test = BOWTest.iloc[:, :-1]
y_test = BOWTest[labelKey]
return X_test, y_test
def runDNNTrain(self, corpus=None, learningRate=0.02, hiddenUnitSize=[128, 256, 128], dataKey='Question', labelKey='y'):
if corpus is None:
corpus = self.prepareDF(excelLocation=TRAIN_EXCEL)
self.dnnObject = DNN(pd_df_train=corpus,
pd_df_test=None,
learning_rate=learningRate,
hidden_units_size=hiddenUnitSize,
dataKey=dataKey,
labelKey=labelKey)
result = self.dnnObject.run()
return result
def runSVM(self, corpus=None):
X, y = self.getX(corpus)
svm = SVMClassifier(X, y)
clf = svm.train()
#y_pred = clf.predict(X)
#y_test_pred = clf.predict(X_test)
#result1 = float(sum((y == y_pred) + 0)) / y_pred.shape[0]
#result2 = float(sum((y_test == y_test_pred) + 0)) / y_test_pred.shape[0]
# print 'SVM Training set accuracy: ', result1
# print 'SVM Test set accuracy: ', result2
return clf
def runLR(self, corpus=None, query=None):
X, y = self.getX(corpus)
lr = LogisticRegClassifier(X, y)
clf = lr.train()
#y_pred = clf.predict(X)
#y_test_pred = clf.predict(X_test)
#result1 = float(sum((y == y_pred) + 0))/y_pred.shape[0]
#result2 = float(sum((y_test == y_test_pred) + 0))/y_test_pred.shape[0]
# print 'LR Training set accuracy: ', result1
# print 'LR Test set accuracy: ', result2
return clf
def getAnswer(self, answer_index):
answer = Corpus().loadData(TRAIN_EXCEL)
return answer['Answer'][answer_index]
