def generate_seq(
model : Model,
seed,
numchars,
size):
ls = seed.shape[0]
# Due to the way Keras RNNs work, we feed the model the
# whole sequence each time, constantly sampling the nect character.
# It's a little bit inefficient, but that doesn't matter much when generating
tokens = np.concatenate([seed, np.zeros(size - ls)])
for i in range(ls, size-1):
toh = util.to_categorical(tokens[None,:], numchars)
probs = model.predict(toh)
# Extract the i-th probability vector and sample an index from it
next_token = sample(probs[0, i, :])
tokens[i+1] = next_token
return [int(t) for t in tokens]
def cal_auc(pred, true, is_torch=True, is_softmax=False, isbin=True):
if is_torch:
true = true.data.cpu().numpy()
pred = pred.data.cpu().numpy()
if is_softmax:
pred = np.argmax(pred, axis=1)
if isbin:
true = to_categorical(true, 2)
# trues = to_categorical(true,2)
auc = metrics.roc_auc_score(true, pred)
return auc
def generate_seq(
model : Model,
seed,
numchars,
size,
temperature=1.0):
"""
:param model: The complete RNN language model
:param seed: The first few wordas of the sequence to start generating from
:param size: The total size of the sequence to generate
:param temperature: This controls how much we follow the probabilities provided by the network. For t=1.0 we just
sample directly according to the probabilities. Lower temperatures make the high-probability words more likely
(providing more likely, but slightly boring sentences) and higher temperatures make the lower probabilities more
likely (resulting is weirder sentences). For temperature=0.0, the generation is _greedy_, i.e. the word with the
highest probability is always chosen.
:return: A list of integers representing a samples sentence
"""
ls = seed.shape[0]
# Due to the way Keras RNNs work, we feed the model a complete sequence each time. At first it's just the seed,
# zero-padded to the right length. With each iteration we sample and set the next character.
tokens = np.concatenate([seed, np.zeros(size - ls)])
# convert the integer sequence to a categorical one
toh = util.to_categorical(tokens[None, :], numchars)
for i in range(ls, size-1):
# predict next characters (for the whole sequence)
probs = model.predict(toh)
# Extract the i-th probability vector and sample an index from it
next_token = util.sample(probs[0, i-1, :], temperature)
tokens[i] = next_token
# update the one-hot encoding
toh[0, i, 0] = 0
toh[0, i, next_token] = 1
return [int(t) for t in tokens]
# Brief: cnn网络结构
import csv
import os
import numpy as np
import tensorflow as tf
from tensorflow.contrib import learn
import config
import data_helpers
from util import to_categorical
if config.eval_all_train_data:
x_raw, y = data_helpers.load_data_labels(config.data_dir)
y = to_categorical(y)
y = np.argmax(y, axis=1)
elif config.infer_data_path:
infer_datas = list(
open(config.infer_data_path, "r", encoding="utf-8").readlines())
infer_datas = [s.strip() for s in infer_datas]
x_raw = data_helpers.load_infer_data(infer_datas)
y = []
else:
x_raw = data_helpers.load_infer_data([
"do you think it is right.", "everything is off.", "i hate you .",
"it is a bad film.", "good man and bad person.",
"价格不是最便宜的，招商还是浦发银行是238*12=2856.00人家还可以分期的。",
u"驱动还有系统要自装,还有显卡太鸡巴低了.还有装系统太麻烦了"
])
y = [1, 0, 0, 0, 1, 0, 1]
def go(options):
lstm_hidden = options.lstm_capacity
print('devices', device_lib.list_local_devices())
if options.task == 'europarl':
dir = options.data_dir
x, numchars, char_to_ix, ix_to_char = \
util.load_char_data(dir+os.sep+'europarl-v8.fi-en.en', limit=options.limit, length=options.sequence_length)
x_max_len = max([len(sentence) for sentence in x])
print('max sequence length ', x_max_len)
print(len(ix_to_char), ' distinct characters')
# x = util.batch_pad(x, options.batch)
x = sequence.pad_sequences(x, x_max_len, padding='post', truncating='post')
def decode(seq):
return ''.join(ix_to_char[id] for id in seq)
else:
raise Exception('Dataset name not recognized.')
print('Data Loaded.')
# print(sum([b.shape[0] for b in x]), ' sentences loaded')
#
# for i in range(3):
# batch = random.choice(x)
# print(batch[0, :])
# print(decode(batch[0, :]))
## Define model
input = Input(shape=(None, numchars))
h = LSTM(lstm_hidden, return_sequences=True)(input)
if options.extra is not None:
for _ in range(options.extra):
h = LSTM(lstm_hidden, return_sequences=True)(h)
out = TimeDistributed(Dense(numchars, activation='softmax'))(h)
model = Model(input, out)
opt = keras.optimizers.Adam(lr=options.lr)
model.compile(opt, 'categorical_crossentropy')
model.summary()
epochs = 0
n = x.shape[0]
x_shifted = np.concatenate([np.ones((n, 1)), x], axis=1) # prepend start symbol
x_shifted = util.to_categorical(x_shifted, numchars)
x_out = np.concatenate([x, np.zeros((n, 1))], axis=1) # append pad symbol
x_out = util.to_categorical(x_out, numchars) # output to one-hots
def generate():
for i in range(CHECK):
b = random.randint(0, n - 1)
seed = x[b, :20]
seed = np.insert(seed, 0, 1)
gen = generate_seq(model, seed, numchars, 120)
print('seed ', decode(seed))
print('out ', decode(gen))
print()
# Train the model
generate_stuff = keras.callbacks.LambdaCallback(
on_epoch_end=lambda epoch, logs: generate())
model.fit(x_shifted, x_out, epochs=options.epochs, batch_size=64, callbacks=[generate_stuff])
import layer
import optimizers
import pickle
import util
import numpy
import matplotlib.pyplot as plt
train_set, val_set, test_set = pickle.load(open("mnist.pkl", "rb"),
encoding='latin1')
model = model.Sequence()
model.add(layer.Dense(300, input_dim=28 * 28, activation="Relu"))
#model.add(layer.Dense(300, activation="Relu"))
model.add(layer.Dense(10))
train_y = util.to_categorical(train_set[1])
idx = numpy.random.choice(train_set[0].shape[0], 50000)
train_set = train_set[0][idx]
train_y = train_y[idx]
model.init()
model.fit(input_data=train_set, output_data=train_y, epoch=500, batch_num=10)
model.compile(optimizer=optimizers.SGD(model, 0.1), loss="Mean_squared_error")
model.train()
id = 0
rightnum = 0
for now in val_set[0]:
# plt.imshow(numpy.reshape(now,(28,28)))
# plt.show()
ans = val_set[1][id]
def process_y(y):
return util.to_categorical(y)
def go(options):
if options.seed < 0:
seed = random.randint(0, 1000000)
print('random seed: ', seed)
np.random.seed(seed)
else:
np.random.seed(options.seed)
## Load the data
if options.task == 'alice':
dir = options.data_dir
x, char_to_ix, ix_to_char = \
util.load_characters(util.DIR + '/datasets/alice.txt', limit=options.limit, length=options.sequence_length)
x_max_len = max([len(sentence) for sentence in x])
numchars = len(ix_to_char)
print(numchars, ' distinct characters found')
x = sequence.pad_sequences(x, x_max_len, padding='post', truncating='post')
elif options.task == 'shakespeare':
dir = options.data_dir
x, char_to_ix, ix_to_char = \
util.load_characters(util.DIR + '/datasets/shakespeare.txt', limit=options.limit, length=options.sequence_length)
x_max_len = max([len(sentence) for sentence in x])
numchars = len(ix_to_char)
print(numchars, ' distinct characters found')
x = sequence.pad_sequences(x, x_max_len, padding='post', truncating='post')
elif options.task == 'file':
dir = options.data_dir
x, char_to_ix, ix_to_char = \
util.load_characters(options.da, limit=options.limit, length=options.sequence_length)
x_max_len = max([len(sentence) for sentence in x])
numchars = len(ix_to_char)
print(numchars, ' distinct characters found')
x = sequence.pad_sequences(x, x_max_len, padding='post', truncating='post')
else:
raise Exception('Dataset name ({}) not recognized.'.format(options.task))
def decode(seq):
return ''.join(ix_to_char[id] for id in seq)
print('Data Loaded.')
## Shape the data. The inputs get a start symbol (1) prepended. We shorten the sequences by one so that the lengths
# match
n = x.shape[0]
x_in = np.concatenate([np.ones((n, 1)), x[:, :-1]], axis=1) # prepend start symbol
x_out = x
assert x_in.shape == x_out.shape
# convert from integer sequences to sequences of one-hot vectors
x_in = util.to_categorical(x_in, numchars)
x_out = util.to_categorical(x_out, numchars) # output to one-hots
## Define the model
input = Input(shape=(None, numchars))
#- We define the model as variable-length (even though all training data has fixed length). This allows us to generate
# longer sequences during inference.
h = LSTM(options.lstm_capacity, return_sequences=True)(input)
if options.extra is not None:
for _ in range(options.extra):
h = LSTM(options.lstm_capacity, return_sequences=True)(h)
# Apply a single dense layer to all timesteps of the resulting sequence to convert back to characters
out = TimeDistributed(Dense(numchars, activation='softmax'))(h)
model = Model(input, out)
opt = keras.optimizers.Adam(lr=options.lr)
model.compile(opt, 'categorical_crossentropy')
#- For each timestep the model outputs a probability distribution over all characters. Categorical crossentopy mean
# that we try to optimize the log-probability of the probability of the correct character (averaged over all
# characters in all sequences.
model.summary()
## Create callback to generate some samples after each epoch
def generate(epoch):
if epoch % options.out_every == 0 and epoch > 0:
for i in range(CHECK):
b = random.randint(0, n - 1)
seed = x[b, :20]
seed = np.insert(seed, 0, 1)
gen = generate_seq(model, seed, numchars, options.gen_length)
print('*** [', decode(seed), '] ', decode(gen[len(seed):]))
print()
# Train the model
generate_stuff = keras.callbacks.LambdaCallback(
on_epoch_end=lambda epoch, logs: generate(epoch))
model.fit(x_in, x_out,
validation_split=1/10,
epochs=options.epochs, batch_size=options.batch,
callbacks=[generate_stuff])
from layers import Conv2D, Dense, Dropout, Activation, Flatten
import matplotlib.pyplot as plt
from sklearn.metrics import accuracy_score
# Data Processing
# mnist = fetch_mldata('MNIST original')
# X = mnist.data
# y = mnist.target
# X = (X.astype(np.float32) - 127.5) / 127.5
dataset = datasets.load_digits()
X = dataset.data
y = dataset.target
y = to_categorical(y.astype("int"))
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3)
# print(X_train.shape)
X_train = X_train[:256]
y_train = y_train[:256]
X_train = X_train.reshape((-1, 1, 8, 8))
X_test = X_test.reshape((-1, 1, 8, 8))
X_test = X_train[:256]
y_test = y_train[:256]
# Model
model = NeuralNetwork(SquareLoss(), (X_test, y_test))
model.add(
Conv2D(16,
filter_shape=(3, 3),
print('id: ', eid)
print('num of epochs:', n_epochs)
#### Load imdb data ####
imdb = np.load('../data/imdb_word_emb.npz')
X_train = imdb['X_train'] #(25000, 80, 128)
Y_train = imdb['y_train'] #(25000, ) => 0 or 1
X_test = imdb['X_test']
Y_test = imdb['y_test']
n_samples = X_train.shape[0]
n_time_steps = X_train.shape[1]
n_input = X_train.shape[2]
n_classes = 2
n_iters = int(n_epochs * n_samples / batch_size)
# transform to one-hot
Y_train = to_categorical(Y_train, 2)
Y_test = to_categorical(Y_test, 2)
# Convert to Dataset instance
train_dataset = Dataset(X_train, Y_train, batch_size)
#### Define RNN Models ####
def RNN(x_sequence, n_hidden):
state = tf.Variable(tf.zeros([batch_size, n_hidden]))
U = tf.Variable(tf.random_normal([n_input, n_hidden], stddev=1))
W = tf.Variable(tf.random_normal([n_hidden, n_hidden], stddev=1))
hidden_bias = tf.Variable(
tf.random_normal([batch_size, n_hidden], stddev=1))
# Unroll timesteps
for x in x_sequence:
state = tf.tanh(tf.matmul(x, U) + tf.matmul(state, W) + hidden_bias)