def RNN(data_train, labels_train, data_test, labels_test, n_features):
"""
Adapted from Passage's sentiment.py at
https://github.com/IndicoDataSolutions/Passage/blob/master/examples/sentiment.py
License: MIT
"""
import numpy as np
import pandas as pd
from passage.models import RNN
from passage.updates import Adadelta
from passage.layers import Embedding, GatedRecurrent, Dense
from passage.preprocessing import Tokenizer
layers = [
Embedding(size=128, n_features=n_features),
GatedRecurrent(size=128, activation='tanh', gate_activation='steeper_sigmoid', init='orthogonal', seq_output=False, p_drop=0.75),
Dense(size=1, activation='sigmoid', init='orthogonal')
]
model = RNN(layers=layers, cost='bce', updater=Adadelta(lr=0.5))
tokenizer = Tokenizer(min_df=10)
X = tokenizer.fit_transform(data)
model.fit(X, labels, n_epochs=10)
predi = model.predit(data_test).flatten
labels_predicted = np.ones(len(data_test))
labels_predicted[predi<0.5] = 0
def __init__(self, **kwargs):
self.emb = kwargs['embedding_size']
del kwargs['embedding_size']
oldRNN.__init__(self, **kwargs)
self.settings['embedding_size'] = self.emb
valY = tensor.dvector('valY')
Ypredict = tensor.dvector('Ypredict')
valcost = self.cost(valY, Ypredict)
self.valcost = function([valY, Ypredict], valcost)
self.thresholds = np.linspace(.5, .8, 8)
def rnn(train_text, train_label):
tokenizer = Tokenizer()
train_tokens = tokenizer.fit_transform(train_text)
layers = [
Embedding(size=50, n_features=tokenizer.n_features),
GatedRecurrent(size=128),
Dense(size=1, activation='sigmoid')
]
# print "train_tokens=", train_tokens
model = RNN(layers=layers, cost='BinaryCrossEntropy')
model.fit(train_tokens, train_label)
return model
def __fit_model(self, X, Y, num_features):
layers = [
Embedding(size=128, n_features=num_features),
GatedRecurrent(size=512, p_drop=0.4),
Dense(size=14, activation='softmax', p_drop=0.2)
]
model = RNN(layers=layers, cost='cce', updater=Adadelta(lr=0.5))
model.fit(X, Y, n_epochs=10)
return model
def build_model(weights=None,
embedding_size=256,
recurrent_gate_size=512,
n_features=5,
dropout=0.4):
"""
build_model
Inputs:
weights - Path to a weights file to load, or None if the model should be built from scratch
embedding_size - Size of the embedding layer
recurrent_gate_size - Size of the gated recurrent layer
n_features - Number of features for the embedding layer
dropout - Dropout value
Returns:
A model object ready for training (or evaluation if a previous model was loaded via `weights`)
"""
# vvvvv
#Modify this if you want to change the structure of the network!
# ^^^^^
model_layers = [
Embedding(size=embedding_size, n_features=n_features),
GatedRecurrent(size=recurrent_gate_size, p_drop=dropout),
Dense(size=1, activation='sigmoid', p_drop=dropout)
]
model = RNN(layers=model_layers,
cost='BinaryCrossEntropy',
verbose=2,
updater='Adam')
if weights: #Just load the provided model instead, I guess?
model = load(weights)
return model
def main(ptrain, ntrain, ptest, ntest, out, modeltype):
assert modeltype in ["gated_recurrent", "lstm_recurrent"]
print("Using the %s model ..." % modeltype)
print("Loading data ...")
trX, trY = load_data(ptrain, ntrain)
teX, teY = load_data(ptest, ntest)
tokenizer = Tokenizer(min_df=10, max_features=100000)
trX = tokenizer.fit_transform(trX)
teX = tokenizer.transform(teX)
print("Training ...")
if modeltype == "gated_recurrent":
layers = [
Embedding(size=256, n_features=tokenizer.n_features),
GatedRecurrent(size=512, activation='tanh', gate_activation='steeper_sigmoid',
init='orthogonal', seq_output=False, p_drop=0.75),
Dense(size=1, activation='sigmoid', init='orthogonal')
]
else:
layers = [
Embedding(size=256, n_features=tokenizer.n_features),
LstmRecurrent(size=512, activation='tanh', gate_activation='steeper_sigmoid',
init='orthogonal', seq_output=False, p_drop=0.75),
Dense(size=1, activation='sigmoid', init='orthogonal')
]
model = RNN(layers=layers, cost='bce', updater=Adadelta(lr=0.5))
model.fit(trX, trY, n_epochs=10)
# Predicting the probabilities of positive labels
print("Predicting ...")
pr_teX = model.predict(teX).flatten()
predY = np.ones(len(teY))
predY[pr_teX < 0.5] = -1
with open(out, "w") as f:
for lab, pos_pr, neg_pr in zip(predY, pr_teX, 1 - pr_teX):
f.write("%d %f %f\n" % (lab, pos_pr, neg_pr))
def train_RNN(tokenizer, tokens, labels): """ INPUT: Trained tokenizer class, label array - The arrays of the tokenized critic reviews and the corresponding labels Returns a trained Recurrent Neural Network class object """ layers = [ Embedding(size=256, n_features=tokenizer.n_features), GatedRecurrent(size=512, activation='tanh', gate_activation='steeper_sigmoid', init='orthogonal', seq_output=False, p_drop=0.75), Dense(size=1, activation='sigmoid', init='orthogonal') ] model = RNN(layers=layers, cost='bce', updater=Adadelta(lr=0.5)) path_snapshots = 'model_snapshots' print "Begin fitting RNN" model.fit(tokens, labels, n_epochs=12) return model
tokenizer = Tokenizer(min_df=10, max_features=100000)
trX = tokenizer.fit_transform(trX)
print("Training data tokenized.")
layers = [
Embedding(size=256, n_features=tokenizer.n_features),
GatedRecurrent(size=512,
activation='tanh',
gate_activation='steeper_sigmoid',
init='orthogonal',
seq_output=False,
p_drop=0.75),
Dense(size=1, activation='sigmoid', init='orthogonal')
]
model = RNN(layers=layers, cost='bce', updater=Adadelta(lr=0.5))
model.fit(trX, trY, n_epochs=10)
te_data = pd.read_csv('testData.tsv', delimiter='\t')
ids = te_data['id'].values
teX = clean(te_data['review'].values)
teX = tokenizer.transform(teX)
pr_teX = model.predict(teX).flatten()
pd.DataFrame(np.asarray([ids,
pr_teX]).T).to_csv('submission.csv',
index=False,
header=["id", "sentiment"])
from passage.layers import Embedding, GatedRecurrent, Dense
from passage.models import RNN
from passage.utils import save
tokenizer = Tokenizer(min_df=10, max_features=50000)
X_train = tokenizer.fit_transform(newsgroups_train.data)
X_test = tokenizer.transform(newsgroups_test.data)
Y_train = newsgroups_train.target
Y_test = newsgroups_test.target
print tokenizer.n_features
layers = [
Embedding(size=128, n_features=tokenizer.n_features),
GatedRecurrent(size=256, activation='tanh', gate_activation='steeper_sigmoid',
init='orthogonal', seq_output=False),
Dense(size=1, activation='sigmoid', init='orthogonal') # sigmoid for binary classification
]
model = RNN(layers=layers, cost='bce') # bce is classification loss for binary classification and sigmoid output
for i in range(2):
model.fit(X_train, Y_train, n_epochs=1)
tr_preds = model.predict(X_train[:len(Y_test)])
te_preds = model.predict(X_test)
tr_acc = metrics.accuracy_score(Y_train[:len(Y_test)], tr_preds > 0.5)
te_acc = metrics.accuracy_score(Y_test, te_preds > 0.5)
print i, tr_acc, te_acc # dataset too small to fully utilize Passage
save(model, 'model.pkl')
def train_and_save_passage_tokenizer_and_rnn_model(x_train,
y_train,
x_test,
character_model=False):
"""Train and save Passage tokenizer and Passage RNN model.
x_train and x_test should each be a series that's already been pre-preocessed: html->text, lowercase, removed
punct/#s
x_train+x_test are used to build the tokenizer.
Note that character-based RNN is a work-in-progress and not actuallly implemented as of now.
"""
# Note that we assume we have train/test reviews that had been preprocessed: html->text, lowercased, removed
# punct/#s
# Note in https://github.com/IndicoDataSolutions/Passage/blob/master/examples/sentiment.py they only
# extract text from html, lowercase and strip (no punctuation removal)
# Tokenization: Assign each word in the reviews an ID to be used in all reviews
tokenizer = Tokenizer(min_df=10,
max_features=100000,
character=character_model)
train_reviews_list = x_train.tolist()
tokenizer.fit(train_reviews_list + x_test.tolist())
# Tokenize training reviws (so can use to fit RNN model on)
train_reviews_tokenized = tokenizer.transform(train_reviews_list)
# Based on https://github.com/vinhkhuc/kaggle-sentiment-popcorn/blob/master/scripts/passage_nn.py which is based
# on https://github.com/IndicoDataSolutions/Passage/blob/master/examples/sentiment.py
# RNN Network:
# -Each tokenized review will be converted into a sequence of words, where each word has an embedding representation
# (256)
# -RNN layer (GRU) attempts to find pattern in sequence of words
# -Final dense layer is used as a logistic classifier to turn RNN output into a probability/prediction
if not character_model:
layers = [
Embedding(size=256, n_features=tokenizer.n_features),
# May replace with LstmRecurrent for LSTM layer
GatedRecurrent(size=512,
activation='tanh',
gate_activation='steeper_sigmoid',
init='orthogonal',
seq_output=False,
p_drop=0.75),
Dense(size=1, activation='sigmoid', init='orthogonal')
]
else:
# Character-level RNN
# Idea is to convert character tokenizations into one-hot encodings in which case
# the embeddings layer is no longer needed
train_reviews_tokenized = map(
lambda r_indexes: pd.get_dummies(
r_indexes, columns=range(tokenizer.n_features + 1)).values,
train_reviews_tokenized)
layers = [
# May replace with LstmRecurrent for LSTM layer
GatedRecurrent(size=100,
activation='tanh',
gate_activation='steeper_sigmoid',
init='orthogonal',
seq_output=False,
p_drop=0.75),
Dense(size=1, activation='sigmoid', init='orthogonal')
]
# RNN classifer uses Binary Cross-Entropy as the cost function
classifier = RNN(layers=layers, cost='bce', updater=Adadelta(lr=0.5))
NUM_EPOCHS = 10
# 10 epochs may take 10+ hours to run depending on machine
classifier.fit(train_reviews_tokenized,
y_train.tolist(),
n_epochs=NUM_EPOCHS)
# Store model and tokenizer
if character_model:
passage.utils.save(classifier, PASSAGE_CHAR_RNN_MODEL)
_ = joblib.dump(tokenizer, PASSAGE_CHAR_TOKENIZER, compress=9)
else:
passage.utils.save(classifier, PASSAGE_RNN_MODEL)
_ = joblib.dump(tokenizer, PASSAGE_TOKENIZER, compress=9)
ntest=1000) # Can increase up to 250K or so
print len(trX), len(trY), len(teX), len(teY)
print teX.shape()
tokenizer = Tokenizer(min_df=10, max_features=50000)
#print trX[1] # see a blog example
trX = tokenizer.fit_transform(trX)
text = "Evropa je v jeho politika naprosto impotent ."
teX = tokenizer.transform(text)
print "number of tokens:" + str(len(trX))
print "number of feathures:" + str(tokenizer.n_features)
layers = [
Embedding(size=256, n_features=tokenizer.n_features),
GatedRecurrent(size=512, p_drop=0.2),
Dense(size=10, activation='softmax', p_drop=0.5)
]
model = RNN(
layers=layers, cost='cce'
) # bce is classification loss for binary classification and sigmoid output
model = load('modelEcho.pkl') # How to load
te_pred = model.predict(teX)
#tr_acc = metrics.accuracy_score(trY[:len(teY)], tr_preds > 0.5)
#te_acc = metrics.accuracy_score(teY, te_preds > 0.5)
print te_pred
tag_end=False, label_noise=0.0)
layers = [
Generic(size=data_dim),
GatedRecurrent(size=32, p_drop=0.1, seq_output=True),
GatedRecurrent(size=32, p_drop=0.1, seq_output=True),
Dense(size=n_classes, activation='softmax', p_drop=0.5)
]
#A bit of l2 helps with generalization, higher momentum helps convergence
# updater = NAG(momentum=0.95, regularizer=Regularizer(l2=1e-4))
updater = Adam(lr=0.006)
padder = Padded(size=batch_size, x_dtype=floatX, y_lag=y_lag)
#Linear iterator for real valued data, cce cost for softmax
model = RNN(layers=layers, updater=updater, iterator=padder, cost='cpsce', Y=T.tensor3())
model.fit(trX, trY, n_epochs=100, batch_size=batch_size)
tr_preds = model.predict(trX[:len(teY)])
te_preds = model.predict(teX)
tr_acc = np.mean([np.all(y == np.argmax(yhat[y_lag:],axis=1)) for y, yhat in zip(trY[:len(teY)], tr_preds)])
te_acc = np.mean([np.all(y == np.argmax(yhat[y_lag:],axis=1)) for y, yhat in zip(teY, te_preds)])
print "Examples from Test: "
S = (["\n".join(map(str, (y, np.argmax(yhat[y_lag:],axis=1))))
for y, yhat in zip(teY, te_preds)])
for s in S[:15]:
print s, "\n"
embedding_sizes = [10, 20, 50, 100, 200, 1000]
gru_sizes = [10, 20, 50, 100, 200, 1000]
epochs = [1, 3, 5, 7, 10]
for embedding_size, gru_size, num_epochs in product(embedding_sizes, gru_sizes, epochs):
X_train, X_test, y_train, y_test = cross_validation.train_test_split(
train_text, train_labels, test_size=0.1, random_state=0
)
layers = [
Embedding(size=embedding_size, n_features=tokenizer.n_features),
GatedRecurrent(size=gru_size),
Dense(size=1, activation="sigmoid"),
]
model = RNN(layers=layers, cost="BinaryCrossEntropy")
model.fit(train_tokens, train_labels, n_epochs=int(num_epochs))
modelfile_name = "stubborn_model.paramsearch.embedding{}.gru{}.epoch{}".format(embedding_size, gru_size, num_epochs)
save(model, modelfile_name + ".pkl")
pickle.dump(tokenizer, open(modelfile_name + "-tokenizer.pkl", "wb"))
results = model.predict(tokenizer.transform(X_test))
count = 0
for r, g in zip(results, y_test):
if int(r >= 0.5) == int(g):
count += 1
results = 1.0 * count / len(y_test)
print(modelfile_name + "\t" + str(results))
return [html.fromstring(text).text_content().lower().strip() for text in texts]
if __name__ == "__main__":
tr_data = pd.read_csv('labeledTrainData.tsv', delimiter='\t')
trX = clean(tr_data['review'].values)
trY = tr_data['sentiment'].values
print("Training data loaded and cleaned.")
tokenizer = Tokenizer(min_df=10, max_features=100000)
trX = tokenizer.fit_transform(trX)
print("Training data tokenized.")
layers = [
Embedding(size=256, n_features=tokenizer.n_features),
GatedRecurrent(size=512, activation='tanh', gate_activation='steeper_sigmoid', init='orthogonal', seq_output=False, p_drop=0.75),
Dense(size=1, activation='sigmoid', init='orthogonal')
]
model = RNN(layers=layers, cost='bce', updater=Adadelta(lr=0.5))
model.fit(trX, trY, n_epochs=10)
te_data = pd.read_csv('testData.tsv', delimiter='\t')
ids = te_data['id'].values
teX = clean(te_data['review'].values)
teX = tokenizer.transform(teX)
pr_teX = model.predict(teX).flatten()
pd.DataFrame(np.asarray([ids, pr_teX]).T).to_csv('submission.csv', index=False, header=["id", "sentiment"])
layers = [
Embedding(size=128, n_features=num_feats),
#LstmRecurrent(size=32),
#NOTE - to use a deep RNN, you need all but the final layers with seq_ouput=True
#GatedRecurrent(size=128, seq_output=True),
#GatedRecurrent(size=256, direction= 'backward' if REVERSE else 'forward'),
GatedRecurrent(size=128, seq_output=True),
GatedRecurrent(size=128),
#Dense(size=64, activation='sigmoid'),
Dense(size=len(lst_freq_tags), activation='sigmoid'),
]
#emd 128, gru 32/64 is good - 0.70006 causer
print("Creating Model")
model = RNN(layers=layers, cost='bce')
def find_cutoff(y_test, predictions):
scale = 20.0
min_val = round(min(predictions))
max_val = round(max(predictions))
diff = max_val - min_val
inc = diff / scale
cutoff = -1
best = -1
for i in range(1, int(scale)+1, 1):
val = inc * i
classes = [1 if p >= val else 0 for p in predictions]
r, p, f1 = rpf1(y_test, classes)
trX, teX, trY, teY = load_gender_data(ntrain=10000) # Can increase up to 250K or so
tokenizer = Tokenizer(min_df=10, max_features=50000)
print trX[1] # see a blog example
trX = tokenizer.fit_transform(trX)
teX = tokenizer.transform(teX)
print tokenizer.n_features
layers = [
Embedding(size=128, n_features=tokenizer.n_features),
GatedRecurrent(size=256, activation='tanh', gate_activation='steeper_sigmoid', init='orthogonal', seq_output=False),
Dense(size=1, activation='sigmoid', init='orthogonal') # sigmoid for binary classification
]
model = RNN(layers=layers, cost='bce') # bce is classification loss for binary classification and sigmoid output
for i in range(2):
model.fit(trX, trY, n_epochs=1)
tr_preds = model.predict(trX[:len(teY)])
te_preds = model.predict(teX)
tr_acc = metrics.accuracy_score(trY[:len(teY)], tr_preds > 0.5)
te_acc = metrics.accuracy_score(teY, te_preds > 0.5)
print i, tr_acc, te_acc
save(model, 'save_test.pkl') # How to save
model = load('save_test.pkl') # How to load
tr_preds = model.predict(trX[:len(teY)])
num_feats = len(set(flatten(train_tokens)))
def get_labels(id):
if id == 3:
return [1, 0]
else:
return [0, 1]
seq_labels = map(lambda (l): map(get_labels, l), train_tokens)
layers = [
Embedding(size=128, n_features=num_feats),
GatedRecurrent(size=128, seq_output=True),
Dense(size=num_feats, activation='softmax')
]
#iterator = SortedPadded(y_pad=True, y_dtype=intX)
#iterator = SortedPadded(y_dtype=intX)
#model = RNN(layers=layers, cost='seq_cce', iterator=iterator, Y=T.imatrix())
model = RNN(layers=layers, cost='seq_cce')
#model.fit(train_tokens, [1,0,1])
model.fit(train_tokens, train_tokens)
#model.predict(tokenizer.transform(["Frogs are awesome", "frogs are amphibious"]))
model.predict(train_tokens)
save(model, 'save_test.pkl')
model = load('save_test.pkl')
""" This model, although doing sequential prediction, predicts a tag per document not per word. """
#!/usr/bin/env python
# coding=utf-8
from passage.preprocessing import Tokenizer
from passage.layers import Embedding, GatedRecurrent, Dense
from passage.models import RNN
from passage.utils import save, load
train_text = ['hello world','foo bar']
train_labels = [0,1]
test_text = ['good man']
tokenizer = Tokenizer()
train_tokens = tokenizer.fit_transform(train_text)
layers = [
Embedding(size=128, n_features=tokenizer.n_features),
GatedRecurrent(size=128),
Dense(size=1, activation='sigmoid')
]
model = RNN(layers=layers, cost='BinaryCrossEntropy')
model.fit(train_tokens, train_labels)
print model.predict(tokenizer.transform(test_text))
save(model, 'save_test.pkl')
model = load('save_test.pkl')
import sys
# ---
# ---
print 'loading dataset'
d = Dataset(settings['FN_DATASET'], settings['FN_VOCABULARY'])
d.load()
print 'generating labeled training set'
train_text,train_labels = d.getNextWordPredTrainset(10)
#for t,l in zip(train_text,train_labels):
# print t,'->',l
tokenizer = Tokenizer()
train_tokens = tokenizer.fit_transform(train_text)
save(train_tokens, settings['FN_TRAINED_TOKENIZER'])
layers = [
Embedding(size=128, n_features=tokenizer.n_features),
GatedRecurrent(size=128),
Dense(size=1, activation='sigmoid')
]
model = RNN(layers=layers, cost='BinaryCrossEntropy')
model.fit(train_tokens, train_labels)
save(model, settings['FN_MODEL_NEXTWORDPRED'])
print len(trX), len(trY), len(teX), len(teY)
tokenizer = Tokenizer(min_df=10, max_features=50000)
trX = tokenizer.fit_transform(trX)
pickle.dump(tokenizer, open('tokenizer.pkl', 'wb'))
print "number of tokens:" + str(len(trX))
teX = tokenizer.transform(teX)
print "number of feathures:" + str(tokenizer.n_features)
layers = [
Embedding(size=256, n_features=tokenizer.n_features),
GatedRecurrent(size=725),
Dense(size=10, activation='softmax')
]
model = RNN(layers=layers, cost='cce')
model.fit(trX, trY, n_epochs=10)
save(model, 'modelEcho.pkl')
tr_preds = model.predict(trX)
te_preds = model.predict(teX)
data = pd.DataFrame(trY)
data.to_csv('data/trY.vec')
data = pd.DataFrame(tr_preds)
data.to_csv('data/tr_preds.vec')
tr_acc = np.mean(np.argmax(trY, axis=1) == np.argmax(tr_preds, axis=1))
indexy = np.argmax(teY, axis=1)
data = pd.DataFrame(indexy)
from passage.models import RNN from passage.updates import NAG, Regularizer from passage.layers import Generic, GatedRecurrent, Dense from passage.utils import load, save from load import load_mnist trX, teX, trY, teY = load_mnist() #Use generic layer - RNN processes a size 28 vector at a time scanning from left to right layers = [ Generic(size=28), GatedRecurrent(size=512, p_drop=0.2), Dense(size=10, activation='softmax', p_drop=0.5) ] #A bit of l2 helps with generalization, higher momentum helps convergence updater = NAG(momentum=0.95, regularizer=Regularizer(l2=1e-4)) #Linear iterator for real valued data, cce cost for softmax model = RNN(layers=layers, updater=updater, iterator='linear', cost='cce') model.fit(trX, trY, n_epochs=20) tr_preds = model.predict(trX[:len(teY)]) te_preds = model.predict(teX) tr_acc = np.mean(trY[:len(teY)] == np.argmax(tr_preds, axis=1)) te_acc = np.mean(teY == np.argmax(te_preds, axis=1)) # Test accuracy should be between 98.9% and 99.3% print 'train accuracy', tr_acc, 'test accuracy', te_acc
X_test = tokenizer.transform(newsgroups_test.data)
Y_train = newsgroups_train.target
Y_test = newsgroups_test.target
print tokenizer.n_features
layers = [
Embedding(size=128, n_features=tokenizer.n_features),
GatedRecurrent(size=256,
activation='tanh',
gate_activation='steeper_sigmoid',
init='orthogonal',
seq_output=False),
Dense(size=1, activation='sigmoid',
init='orthogonal') # sigmoid for binary classification
]
model = RNN(
layers=layers, cost='bce'
) # bce is classification loss for binary classification and sigmoid output
for i in range(2):
model.fit(X_train, Y_train, n_epochs=1)
tr_preds = model.predict(X_train[:len(Y_test)])
te_preds = model.predict(X_test)
tr_acc = metrics.accuracy_score(Y_train[:len(Y_test)], tr_preds > 0.5)
te_acc = metrics.accuracy_score(Y_test, te_preds > 0.5)
print i, tr_acc, te_acc # dataset too small to fully utilize Passage
save(model, 'model.pkl')
def train_model(modeltype, delta):
assert modeltype in ["gated_recurrent", "lstm_recurrent"]
print "Begin Training"
df_imdb_reviews = pd.read_csv('../data/imdb_review_data.tsv', escapechar='\\', delimiter='\t')
X = clean(df_imdb_reviews['review'].values)
y = df_imdb_reviews['sentiment'].values
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.5, random_state=42)
print "Tokenize"
tokenizer = Tokenizer(min_df=10, max_features=100000)
X_train = tokenizer.fit_transform(X_train)
X_train = [[float(x) for x in y] for y in X_train]
X_test = tokenizer.transform(X_test)
X_test = [[float(x) for x in y] for y in X_test]
print "Number of featers: {}".format(tokenizer.n_features)
print "Training model"
if modeltype == "gated_recurrent":
layers = [
Embedding(size=256, n_features=tokenizer.n_features),
GatedRecurrent(size=512, activation='tanh', gate_activation='steeper_sigmoid',
init='orthogonal', seq_output=True, p_drop=0.5),
Dense(size=1, activation='sigmoid', init='orthogonal')
]
else:
layers = [
Embedding(size=256, n_features=tokenizer.n_features),
LstmRecurrent(size=512, activation='tanh', gate_activation='steeper_sigmoid',
init='orthogonal', seq_output=True, p_drop=0.5),
Dense(size=1, activation='sigmoid', init='orthogonal')
]
# bce is classification loss for binary classification and sigmoid output
model = RNN(layers=layers, cost='bce', updater=Adadelta, (lr=delta))
model.fit(X_train, y_train, n_epochs=20)
with open('../data/{}_tokenizer_delta_{}_pdrop_0.5.pkl'.format(modeltype, delta), 'w') as f:
vectorizer = pickle.dump(tokenizer, f)
with open('../data/{}_model_delta_{}._pdrop_0.5.pkl'.format(modeltype, delta), 'w') as f:
model = pickle.dump(model, f)
try:
y_pred_te = model.predict(X_test).flatten() >= 0.5
y_pred_tr = model.predict(X_train).flatten() >= 0.5
print 'Test Accuracy: {}'.format(accuracy_score(y_test,y_pred_te))
print 'Test Precision: {}'.format(precision_score(y_test,y_pred_te))
print 'Test Recall: {}'.format(recall_score(y_test,y_pred_te))
print 'Train Accuracy: {}'.format(accuracy_score(y_train,y_pred_tr))
print 'Train Precision: {}'.format(precision_score(y_train,y_pred_tr))
print 'Train Recall: {}'.format(recall_score(y_train,y_pred_tr))
except:
print "Unable to perform metrics"
return tokenizer, model
print "Loading tagger..."
taggerMorp = Tagger.load(sys.argv[1])
if not taggerMorp:
sys.stderr.write("Cannot load tagger from file '%s'\n" % sys.argv[1])
sys.exit(1)
# Init RNN
print "Loading RNN..."
tokenizerRNN = pickle.load(open('tokenizer.pkl', 'rb'))
layers = [
Embedding(size=256, n_features=tokenizerRNN.n_features),
GatedRecurrent(size=512, p_drop=0.2),
Dense(size=10, activation='softmax', p_drop=0.5)
]
model = RNN(
layers=layers, cost='cce'
) # bce is classification loss for binary classification and sigmoid output
model = load(sys.argv[2]) # How to load
print "RNN loaded"
s.listen(10)
print 'Socket now listening'
#######################################################################################################
while 1:
conn, addr = s.accept()
print 'Connected with ' + addr[0] + ':' + str(addr[1])
start_new_thread(clientthread, (conn, ))
s.close()
