def myfunc(authorlist, doc_id, tdoc):
subset = None
#Whether to save model parameters
save = False
model_name_path = 'params/crepe_model.json'
model_weights_path = 'params/crepe_model_weights.h5'
#Maximum length. Longer gets chopped. Shorter gets padded.
maxlen = 1014
#Model params
#Filters for conv layers
nb_filter = 128 #initially 256
#Number of units in the dense layer
dense_outputs = 512 #Initially 1024
#Conv layer kernel size
filter_kernels = [3, 3, 3, 3, 3, 3]
#Number of units in the final output layer. Number of classes.
#Compile/fit params
batch_size = 32
nb_epoch = 10
print('Loading data...')
#Expect x to be a list of sentences. Y to be a one-hot encoding of the
#categories.
### 515-1122-122 and 1573 with remove 6 layers
#authorlist=[121, 479 , 649 ]
#doc_id = 14706
#authorlist=[114, 1492, 124, 1228]
#doc_id = [206, 205]
cat_output = len(authorlist) #binary in the last layer
# def main(authorlist, doc_id):
((trainX, trainY), (valX,
valY)) = data_helpers.load_ag_data(authors=authorlist,
docID=doc_id,
tdoc=tdoc)
print('Creating vocab...')
vocab, reverse_vocab, vocab_size, check = data_helpers.create_vocab_set()
#trainX = data_helpers.encode_data(trainX, maxlen, vocab, vocab_size, check)
#test_data = data_helpers.encode_data(valX, maxlen, vocab, vocab_size, check)
print('Build model...')
classes = len(authorlist)
(model, sgd,
model_weights_path) = py_crepe.build_model(classes, filter_kernels,
dense_outputs, maxlen,
vocab_size, nb_filter)
vocab_size
print('Fit model...')
initial = datetime.datetime.now()
for e in xrange(nb_epoch):
xi, yi = data_helpers.shuffle_matrix(trainX, trainY)
xi_test, yi_test = data_helpers.shuffle_matrix(valX, valY)
if subset:
batches = data_helpers.mini_batch_generator(xi[:subset],
yi[:subset],
vocab,
vocab_size,
check,
maxlen,
batch_size=batch_size)
else:
batches = data_helpers.mini_batch_generator(xi,
yi,
vocab,
vocab_size,
check,
maxlen,
batch_size=batch_size)
test_batches = data_helpers.mini_batch_generator(xi_test,
yi_test,
vocab,
vocab_size,
check,
maxlen,
batch_size=batch_size)
accuracy = 0.0
loss = 0.0
step = 1
start = datetime.datetime.now()
print('Epoch: {}'.format(e))
for x_train, y_train in batches:
f = model.train_on_batch(x_train, y_train)
loss += f[0]
loss_avg = loss / step
accuracy += f[1]
accuracy_avg = accuracy / step
if step % 100 == 0:
print(' Step: {}'.format(step))
print('\tLoss: {}. Accuracy: {}'.format(
loss_avg, accuracy_avg))
step += 1
test_accuracy = 0.0
test_loss = 0.0
test_step = 1
for x_test_batch, y_test_batch in test_batches:
f_ev = model.test_on_batch(x_test_batch, y_test_batch)
test_loss += f_ev[0]
test_loss_avg = test_loss / test_step
test_accuracy += f_ev[1]
test_accuracy_avg = test_accuracy / test_step
test_step += 1
stop = datetime.datetime.now()
e_elap = stop - start
t_elap = stop - initial
print(
'Epoch {}. Loss: {}. Accuracy: {}\nEpoch time: {}. Total time: {}\n'
.format(e, test_loss_avg, test_accuracy_avg, e_elap, t_elap))
if save:
print('Saving model params...')
json_string = model.to_json()
with open(model_name_path, 'w') as f:
json.dump(json_string, f)
model.save_weights(model_weights_path)
import cPickle as pickle
with open('sgd.pickle', 'wb') as handle:
pickle.dump(sgd, handle, protocol=pickle.HIGHEST_PROTOCOL)
del trainX, trainY, valX, valY
model.load_weights(model_weights_path)
#from keras.optimizers import SGD
#sgd = SGD(lr=0.01, momentum=0.9, nesterov= True)
# Compile model again (required to make predictions)
model.compile(loss='categorical_crossentropy',
optimizer=sgd,
metrics=['accuracy'])
def predictModel(model, testX):
# Function to take input of data and return prediction model
predY = np.array(model.predict(testX))
predYList = predY[:]
entro = []
flag = False
import math
for row in predY:
entroval = 0
for i in row:
if (i <= 0):
flag = True
pass
else:
entroval += (i * (math.log(i, 2)))
entroval = -1 * entroval
entro.append(entroval)
if (flag == False):
yx = zip(entro, predY)
yx = sorted(yx, key=lambda t: t[0])
newPredY = [x for y, x in yx]
predYEntroList = newPredY[:int(len(newPredY) * 0.3)] # Reduce this
predY = np.mean(predYEntroList, axis=0)
else:
predY = np.mean(predYList, axis=0)
return (predYList, predY)
test_binary = []
#d = []
#d.append(doc_id)
#for docs in d:
(testX, testY) = data_helpers.load_doc_data(authors=authorlist,
docID=doc_id)
testX = data_helpers.encode_data(testX, maxlen, vocab, vocab_size, check)
(predYList, predY) = predictModel(model, testX)
testY = np.array(testY)
testY = testY.mean(axis=0)
predLocation = predY.tolist().index(max(predY))
if predLocation == testY:
test_binary.append(1)
else:
test_binary.append(0)
from IPython.display import clear_output
clear_output()
#predY = np.array(model.predict(testX, batch_size=batch_size))
#predY
#predY.mean(axis = 0)
return test_binary
"""
import data_helpers maxlen = 9 vocab, _, vocab_size, check = data_helpers.create_vocab_set() x_test = ['this is awesome'] res = data_helpers.encode_data(x_test, maxlen, vocab, 69, check) print(res)
#Compile/fit params
batch_size = 80
nb_epoch = 10
# In[10]:
print('Loading data...')
#Expect x to be a list of sentences. Y to be a one-hot encoding of the
#categories.
(xt, yt), (x_test, y_test) = data_helpers.load_ag_data()
print('Creating vocab...')
vocab, reverse_vocab, vocab_size, check = data_helpers.create_vocab_set()
test_data = data_helpers.encode_data(x_test, maxlen, vocab, vocab_size, check)
print('Build model...')
model = py_crepe.model(filter_kernels, dense_outputs, maxlen, vocab_size,
nb_filter, cat_output)
# In[4]:
print('Fit model...')
initial = datetime.datetime.now()
for e in xrange(nb_epoch):
xi, yi = data_helpers.shuffle_matrix(xt, yt)
xi_test, yi_test = data_helpers.shuffle_matrix(x_test, y_test)
if subset:
batches = data_helpers.mini_batch_generator(xi[:subset],
# Compile/fit params
batch_size = 80
nb_epoch = 20
print('Loading data...')
# Expect x to be a list of sentences. Y to be index of the categories.
(xt, yt), (x_test, y_test) = data_helpers.load_ag_data()
print('Creating vocab...')
vocab, reverse_vocab, vocab_size, alphabet = data_helpers.create_vocab_set()
print('Build model...')
model = py_crepe.create_model(filter_kernels, dense_outputs, maxlen, vocab_size,
nb_filter, cat_output)
# Encode data
xt = data_helpers.encode_data(xt, maxlen, vocab)
x_test = data_helpers.encode_data(x_test, maxlen, vocab)
print('Chars vocab: {}'.format(alphabet))
print('Chars vocab size: {}'.format(vocab_size))
print('X_train.shape: {}'.format(xt.shape))
model.summary()
print('Fit model...')
model.fit(xt, yt,
validation_data=(x_test, y_test), batch_size=batch_size, epochs=nb_epoch, shuffle=True)
if save:
print('Saving model params...')
json_string = model.to_json()
with open(model_name_path, 'w') as f:
json.dump(json_string, f)
dense_outputs = 2048
# Compile/fit params
batch_size = 128
nb_epoch = 200
print('Loading data...')
# Expect x to be a list of sentences. Y to be a one-hot encoding of the categories.
(xt, yt), (x_test, y_test), label_array = data_helpers.load_ag_data()
# Number of units in the final output layer. Number of classes.
cat_output = len(label_array) # 1007
print('Creating vocab...')
vocab, reverse_vocab, vocab_size, alphabet = data_helpers.create_vocab_set()
xt = data_helpers.encode_data(xt, maxlen, vocab)
x_test = data_helpers.encode_data(x_test, maxlen, vocab)
print('Chars vocab: {}'.format(alphabet))
print('Chars vocab size: {}'.format(vocab_size))
print('X_train.shape: {}'.format(xt.shape))
print('Build model...')
model = char_cnn_model.model(filter_kernels, dense_outputs, maxlen, vocab_size,
nb_filter, cat_output)
print('Fit model...')
model.summary()
model.fit(xt,
# Filters for conv layers
nb_filter = 512
# Conv layer kernel size
filter_kernels = [7, 7, 3, 3]
lg.info('Loading data...')
# Expect x to be a list of sentences. Y to be a one-hot encoding of the categories.
(xt, yt), (x_test, y_test) = data_helpers.load_restoclub_data_for_encdec("data")
lg.info('Creating vocab...')
vocab, reverse_vocab, vocab_size, check = data_helpers.create_vocab_set()
lg.info(str(vocab))
test_data = data_helpers.encode_data(x_test, maxlen, vocab, vocab_size, check)
lg.info('Build model...')
autoencoder_model, encoder_model = tweet2vec.model(filter_kernels,
maxlen,
vocab_size,
nb_filter,
latent_dim,
latent_dim,
rnn_type)
lg.info('Fit model...')
initial = datetime.datetime.now()
for e in range(nb_epoch):