class JointSequentialIntentModel(IntentExtractionModel):
"""
Joint Intent classification and Slot tagging Model
"""
def __init__(self):
super(JointSequentialIntentModel, self).__init__()
def build(self,
sentence_length,
vocab_size,
tag_labels,
intent_labels,
token_emb_size=100,
tagger_hidden=100,
tagger_dropout=0.5,
intent_classifier_hidden=100,
emb_model_path=None):
"""
Build the model
Args:
sentence_length (int): max length of a sentence
vocab_size (int): vocabulary size
tag_labels (int): number of tag labels
intent_labels (int): number of intent labels
token_emb_size (int): token embedding vectors size
tagger_hidden (int): label tagger LSTM hidden size
tagger_dropout (float): label tagger dropout rate
intent_classifier_hidden (int): intent LSTM hidden size
emb_model_path (str): external embedding model path
"""
tokens_input, token_emb = self._create_input_embed(sentence_length,
emb_model_path is not None,
token_emb_size,
vocab_size)
intent_enc = Bidirectional(LSTM(intent_classifier_hidden))(token_emb)
intent_out = Dense(intent_labels, activation='softmax',
name='intent_classifier')(intent_enc)
intent_vec_rep = RepeatVector(sentence_length)(intent_out)
slot_emb = Bidirectional(LSTM(tagger_hidden, return_sequences=True))(token_emb)
tagger_features = concatenate([slot_emb, intent_vec_rep], axis=-1)
tagger = Bidirectional(
LSTM(tagger_hidden, return_sequences=True))(tagger_features)
tagger = Dropout(tagger_dropout)(tagger)
tagger_out = TimeDistributed(
Dense(tag_labels, activation='softmax'),
name='slot_tag_classifier')(tagger)
self.model = Model(inputs=tokens_input, outputs=[
intent_out, tagger_out])
self.model.compile(optimizer='rmsprop', loss='categorical_crossentropy',
loss_weights=[1., 1.], metrics=['categorical_accuracy'])
def get_model():
'''stack ensemble by NN model
'''
input_layer = Input(shape=get_ensemble_inputShape())
layer = Dense(units=128, activation='relu')(input_layer)
layer = BatchNormalization()(layer)
layer = Dropout(0.3)(layer)
output_layer = Dense(6, activation='sigmoid')(layer)
model = Model(inputs=input_layer, outputs=output_layer)
model.compile(loss='binary_crossentropy', optimizer='rmsprop', metrics=['acc'])
return model
numerator = tf.reduce_sum(onehots_true * probabilities, axis=0)
denominator = tf.reduce_sum(onehots_true + probabilities, axis=0)
loss = 1.0 - 2.0 * (numerator + 1) / (denominator + 1)
return loss
def main()
with open(args.config, 'r') as f:
yam = yaml.load(f)
img_path = yam['img_path']
mask_path = yam['mask_path']
epochs = yam['epochs']
image_size = yam['image_size']
start_neurons = yam['start_neurons']
batch_size = yam['batch_size']
get_custom_objects().update({'lrelu': Activation(tf.keras.layers.LeakyReLU(alpha=0.3))})
train_generator = directory_to_generator(img_path , mask_path , image_size)
steps_per_epoch = int( np.ceil(train_generator.shape[0] / batch_size) )
input_layer = Input((image_size, image_size, 3))
output_layer = build_model(input_layer, start_neurons)
model = Model(input_layer, output_layer)
model.compile(loss = dice_loss, optimizer='adam', metrics=["accuracy"])
model.fit(train_generator , epochs = epochs , steps_per_epoch = steps_per_epoch , batch_size = batch_size)
if __name__ == "__main__":
main()
def ctc_lambda_func( args ):
y_pred, labels, label_lengths = args
y_pred_len = [ [y_pred.shape[1] ] ] * batchSize
# y_pred = y_pred[:, 2:, :]
return K.ctc_batch_cost( labels, K.softmax( y_pred ), y_pred_len, label_lengths )
labels = Input(name='the_labels', shape=[ labelWidth ], dtype='int32')
images = Input(name='the_images', shape=[ targetH, targetW, 1 ], dtype='float32')
label_lengths = Input(name='label_lengths', shape=[1], dtype='int32')
y_pred = backBone( images )
loss_out = Lambda(ctc_lambda_func, output_shape=(1,), name='ctc')( [ y_pred, labels, label_lengths ])
fullModel = Model( inputs=[ images, labels, label_lengths ], outputs=loss_out )
# plot_model(fullModel, to_file='model2.png', show_shapes=True)
fullModel.compile(loss={'ctc': lambda y_true, y_pred: y_pred}, optimizer=optimizer, metrics=['accuracy'])
train_loader = DataGenerator( opt.traindata, batchSize=opt.batchSize, limit=opt.traindata_limit, cache=opt.traindata_cache )
test_loader = DataGenerator( opt.valdata, batchSize=opt.batchSize, limit=opt.valdata_limit, cache=opt.valdata_cache )
# import pdb; pdb.set_trace();
# import IPython as x; x.embed()
class WeightsSaver(Callback):
def __init__(self):
self.fname = opt.outfile + '_' + datetime.now().strftime('%d%m%Y_%H%M%S')
self.i = 1
self.j = 1
self.saveInterval = 100 if 'SAVE_INTERVAL' not in environ else int( environ['SAVE_INTERVAL'])
from keras import Input from keras import layers from keras import Model input_tensor=Input(shape=(64,)) x=layers.Dense(32,activation='relu')(input_tensor) x=layers.Dense(32,activation='relu')(x) output_tensor=layers.Dense(10,activation='softmax')(x) model=Model(input_tensor,output_tensor) # modle.summary() model.compile(optimizer='rmsprop',loss='sparse_categorical_crossentropy',metrics=['acc']) import numpy as np x_train=np.random.random((1000,64)) y_train=np.random.randint(0,10,1000) model.fit(x_train,y_train,epochs=10,batch_size=128) score=model.evaluate(x_train,y_train)
def autoencode(pipe: Pipe,
layer_config: List[Dict],
from_file: str,
store_model: str,
loss: str,
optimiser: str,
epochs: int,
batch_size: int,
shuffle: bool,
validation_split: float,
adjust_weights: float,
mode: str):
"""Build and train an autoencoder."""
import keras
from keras import regularizers, Sequential, Input, Model
from keras.callbacks import EarlyStopping, TensorBoard
from keras.engine import InputLayer
from keras.engine.saving import model_from_yaml, model_from_json
from keras.layers import Dense
from numpy.random.mtrand import seed
from tensorflow import set_random_seed
from lyner.keras_extras import SignalHandler
seed(1)
set_random_seed(2)
matrix = pipe.matrix.copy()
if matrix.isnull().values.any():
LOGGER.warning("Dropping rows containing nan values")
matrix.dropna(how='any', inplace=True)
def parse_layout(layer_conf):
get_layer_type = lambda t: getattr(keras.layers, t, None)
regdict = {'l1_l2': regularizers.l1_l2, 'l1': regularizers.l1, 'l2': regularizers.l2}
lc = layer_conf.copy()
layer_type = lc.get('type', None)
if layer_type:
lc['type'] = get_layer_type(layer_type)
# TODO parse regularizers
kernel_reg_type = lc.get('kernel_regularizer', None)
if kernel_reg_type:
if '(' in kernel_reg_type and ')' in kernel_reg_type:
params = kernel_reg_type[kernel_reg_type.index('(') + 1:kernel_reg_type.index(')')]
if '+' in params:
params = params.split('+')
else:
params = [params]
params = [float(p) for p in params]
kernel_reg_type = kernel_reg_type[:kernel_reg_type.index('(')]
lc['kernel_regularizer'] = regdict[kernel_reg_type](*params)
return lc.pop('type'), int(lc.pop('n')), lc
layout = [parse_layout(layer_conf) for layer_conf in layer_config]
labels = matrix.columns.values.tolist()
data = matrix.values
shape = (data.shape[0],)
data = data.transpose()
if layout:
encoding_dim = layout[-1][1]
encoder = Sequential(name="encoder")
encoder.add(InputLayer(shape, name="encoder_input"))
for layer_num, (Layer, n_nodes, extra_args) in enumerate(layout):
encoder.add(Layer(n_nodes, name=f"encoder_{layer_num}_{n_nodes}", **extra_args))
# kernel_regularizer=regularizers.l1_l2(0.001, 0.001),
# kernel_regularizer=regularizers.l1(0.0001),
decoder = Sequential(name="decoder")
decoder.add(InputLayer((encoding_dim,), name="decoder_input"))
for layer_num, (Layer, n_nodes, _) in enumerate(layout[::-1][1:]):
decoder.add(Layer(n_nodes, name=f"decoder_{layer_num}_{n_nodes}"))
decoder.add(Dense(shape[0], activation='linear', name="decoder_output"))
input_layer = Input(shape=shape, name="autoencoder_input")
encode_layer = encoder(input_layer)
decode_layer = decoder(encode_layer)
autoencoder = Model(input_layer, decode_layer)
if store_model:
if store_model.endswith('.yaml'):
model_string = autoencoder.to_yaml()
elif store_model.endswith('.json'):
model_string = autoencoder.to_json()
else:
model_string = autoencoder.to_yaml()
with open(store_model, 'wt') as writer:
writer.write(model_string)
elif from_file:
with open(from_file, 'rt') as reader:
model_string = '\n'.join(reader.readlines())
if from_file.endswith('.yaml'):
autoencoder = model_from_yaml(model_string)
elif from_file.endswith('.json'):
autoencoder = model_from_json(model_string)
# TODO set encoder and decoder correctly
else:
raise ValueError("No model specified. Use either of --layer-config or --from-file.")
# from pprint import pprint
# pprint(autoencoder.get_config())
autoencoder.compile(optimizer=optimiser, loss=loss, metrics=['mse'], )
early_stopping = EarlyStopping(monitor='val_loss', min_delta=0.0000001, patience=50)
sh = SignalHandler()
autoencoder.fit(np.vsplit(data, 1), np.vsplit(data, 1),
callbacks=[TensorBoard(log_dir='/tmp/autoencoder'), sh, early_stopping],
epochs=epochs,
batch_size=batch_size,
validation_split=validation_split,
shuffle=shuffle
)
sh.uninit()
class Autoencoder:
def __init__(self, encoder=None, decoder=None):
self._encoder = encoder
self._decoder = decoder
def inverse_transform(self, data):
return self._decoder.predict(data).transpose()
def transform(self, data):
return self._encoder.predict(data).transpose()
pipe.decomposition = Autoencoder(encoder, decoder)
encoded_data = pipe.decomposition.transform(data)
decoded_data = pipe.decomposition.inverse_transform(encoded_data.T)
pre_error = ((data.T - decoded_data) ** 2).mean(axis=None)
print(f"MSE: {pre_error}")
pipe._index = pipe.matrix.index
pipe._columns = pipe.matrix.columns
if adjust_weights:
quant = float(adjust_weights)
for i, layer in enumerate(encoder.layers):
W, b = layer.get_weights()
low, median, high = np.quantile(W.flatten(), [quant, 0.5, 1 - quant])
W_low = W * (W < low)
W_high = W * (W > high)
selected_weights = W_low + W_high
# oplot([Histogram(x=W.flatten()), Histogram(x=W[W < low].flatten()), Histogram(x=W[W > high].flatten())])
layer.set_weights([selected_weights, b])
break
encoded_data = pipe.decomposition.transform(data)
decoded_data = pipe.decomposition.inverse_transform(encoded_data.T)
post_error = ((data.T - decoded_data) ** 2).mean(axis=None)
print(f"MSE: {post_error}")
if 'weights' == mode:
layer = 0
layer_weights = encoder.layers[layer].get_weights()
layer = encoder.layers[layer]
if len(layer_weights) == 0:
layer_weights = encoder.layers[0].get_weights()
if len(layer_weights) >= 2:
layer_weights = layer_weights[:-1] # last one is bias
new_data = layer_weights[0]
index = [f'Weight_{i}' for i in range(new_data.shape[0])]
num_nodes = new_data.shape[1]
columns = [f"{layer.name}_{i}" for i in range(num_nodes)]
elif 'nodes' == mode:
new_data = encoder.predict(np.vsplit(data, 1)).transpose()
columns = labels
index = [f"{mode}_{i}" for i in range(encoding_dim)]
elif 'discard' == mode:
W, b = encoder.layers[0].get_weights()
W = np.sum(np.abs(W), axis=1)
W[W != 0] = 1
print(f"Kept {np.sum(W)} weights")
v: np.array = pipe.matrix.values
new_data = (v.T * W).T
columns = pipe.matrix.columns
index = pipe.matrix.index
else:
raise ValueError(f"Unknown mode {mode}")
pipe.matrix = pd.DataFrame(data=new_data,
columns=columns,
index=index,
)
return
def main():
config = InferenceConfig()
# config.display()
# Create Mask Model
mask_model = modellib.MaskRCNN(mode='inference',
model_dir=MASK_LOGS_DIR,
config=config)
mask_model.load_weights(COCO_MODEL_PATH, by_name=True)
# Freeze model
mask_model.keras_model.trainable = False
class_names = CLASS_NAMES
file_names = next(os.walk(IMAGE_DIR))[2]
random.shuffle(file_names)
if SHOW is True:
test_file_names = next(os.walk(TESTING_DIR))[2][0:1]
else:
test_file_names = next(os.walk(TESTING_DIR))[2][0:10]
def log_auto_color(s):
try:
log_file = open("loss_hist_bc_lab.txt", 'a')
log_file.write('{}\n'.format(s))
# print("{}: {}".format(str(datetime.now()), s)) # For debugging
except:
None
finally:
log_file.close()
def report_loss(filenames=test_file_names):
X_batch, Y_batch, _ = generate_training_datum(filenames,
image_dir=TESTING_DIR)
loss = model.evaluate(X_batch, Y_batch)
print("Loss(bachnorm): {}".format(loss))
log_auto_color(loss)
def colorize(filename='000000000643.jpg'):
"""Colorize One picture"""
X_batch, Y_batch, images = generate_training_datum(
[filename], image_dir=TESTING_DIR)
# plt.imshow(images[0])
# plt.show()
preds = model.predict(X_batch)
# print("predict shape: {}".format(preds.shape))
lab = rgb2lab(images[0])
pred_image = np.zeros(lab.shape)
pred_image[:, :, 0] = lab[:, :, 0]
pred_image[:, :, 1:] = preds[0] * 128
# pred_image = np.concatenate((images[0], lab2rgb(pred_image)), axis=1) # Demage orignal image
pred_image = lab2rgb(pred_image)
# print(preds)
if SHOW is True:
plt.imshow(pred_image)
plt.show()
return pred_image
def get_feature_map(images):
""" Get the feature map from the trained mask_rcnn """
result = mask_model.run_graph(
images,
[
('P2', mask_model.keras_model.get_layer('fpn_p2').output
), # -> shape: (2, 256, 256, 256)
('P3', mask_model.keras_model.get_layer('fpn_p3').output
), # -> shape: (2, 128, 128, 256)
('P4', mask_model.keras_model.get_layer('fpn_p4').output
), # -> shape: (2, 64, 64, 256)
('P5', mask_model.keras_model.get_layer('fpn_p5').output
), # -> shape: (2, 32, 32, 256)
])
return result
def generate_training_datum(filenames, image_dir=IMAGE_DIR):
images = []
grayscaled_rgbs = []
Y_batch = []
for filename in filenames:
image = skimage.io.imread(os.path.join(image_dir, filename))
image, _, _, _ = utils.resize_image(image,
min_dim=config.IMAGE_MAX_DIM)
try:
image = image[:config.IMAGE_SHAPE[0], :config.
IMAGE_SHAPE[1], :]
except IndexError:
continue
images.append(image)
lab = rgb2lab(image)
grayscaled_rgb = gray2rgb(rgb2gray(image))
grayscaled_rgbs.append(grayscaled_rgb)
Y_batch.append(lab[:, :, 1:] / 128)
feature_maps = get_feature_map(grayscaled_rgbs)
# print(feature_maps['P2'].shape) # -> (batch_size, pool_size, pool_size, filter_num)
grayscaled_rgbs = np.asarray(grayscaled_rgbs)
Y_batch = np.asarray(Y_batch)
# print(grayscaled_rgbs.shape) # -> (batch_size, height, width, channels)
return feature_maps, Y_batch, images
# generate_training_datum(file_names[0:2])
# ========= Building the network ========= #
# Input: https://stackoverflow.com/questions/44747343/keras-input-explanation-input-shape-units-batch-size-dim-etc
P5 = Input(shape=(
32,
32,
256,
), name='P5')
P4 = Input(shape=(
64,
64,
256,
), name='P4')
P3 = Input(shape=(
128,
128,
256,
), name='P3')
P2 = Input(shape=(
256,
256,
256,
), name='P2')
initer = keras.initializers.RandomUniform(minval=-0.5, maxval=0.5)
# activer = 'relu'
activer = 'sigmoid'
# error = 'mse'
error = 'mean_absolute_error'
# Decode
decode_p5 = KL.Conv2D(128, (3, 3),
padding='same',
bias_initializer=initer,
activation=activer,
name='decode_p5')(P5)
# decode_p5 = KL.TimeDistributed(BatchNorm(axis=3), name='p5_bn')(decode_p5)
# decode_p5 = KL.Activation('relu')(decode_p5)
decode_p5 = UpSampling2D((2, 2))(decode_p5)
decode_p4 = Conv2D(128, (1, 1), padding='same',
bias_initializer=initer)(P4)
decode_p4_5 = Add()([decode_p5, decode_p4])
decode_p4_5 = BatchNorm(axis=3, name='p45_bn')(decode_p4_5)
decode_p4_5 = KL.Activation(activer)(decode_p4_5)
decode2_p4_5 = Conv2D(64, (3, 3),
activation=activer,
padding='same',
bias_initializer=initer)(decode_p4_5)
decode2_p4_5 = UpSampling2D((2, 2))(decode2_p4_5)
decode2_p3 = Conv2D(64, (3, 3), padding='same',
bias_initializer=initer)(P3)
decode2_p3_4_5 = Add()([decode2_p4_5, decode2_p3])
decode2_p3_4_5 = BatchNorm(axis=3, name='p345_bn')(decode2_p3_4_5)
decode2_p3_4_5 = KL.Activation(activer)(decode2_p3_4_5)
decode3_p345 = Conv2D(32, (3, 3), activation=activer,
padding='same')(decode2_p3_4_5)
decode3_p345 = UpSampling2D((2, 2))(decode3_p345)
decode3_p2 = Conv2D(32, (1, 1), padding='same',
bias_initializer=initer)(P2)
decode3_p2345 = Add()([decode3_p345, decode3_p2])
decode3_p2345 = BatchNorm(axis=3, name='p2345_bn')(decode3_p2345)
decode3_p2345 = KL.Activation(activer)(decode3_p2345)
decode_out = Conv2D(16, (3, 3),
activation=activer,
padding='same',
bias_initializer=initer)(decode3_p2345)
decode_out = UpSampling2D((2, 2))(decode_out)
decode_out = Conv2D(4, (3, 3),
activation=activer,
padding='same',
bias_initializer=initer)(decode_out)
decode_out = UpSampling2D((2, 2))(decode_out)
decode_out = Conv2D(2, (3, 3),
activation='tanh',
padding='same',
bias_initializer=initer)(decode_out)
# build
tensorboard = TensorBoard(log_dir=TB_LOG_DIR)
model = Model(inputs=[P5, P4, P3, P2], outputs=decode_out)
if os.path.isfile('auto_color_batch_norm_lab.h5'):
print('Found weights')
model.load_weights('auto_color_batch_norm_lab.h5')
sgd = optimizers.SGD(lr=0.005, momentum=0.1, decay=0.0, nesterov=False)
model.compile(optimizer=sgd, loss=error)
# ========= Training =========== #
batch_size = BATCH_SIZE
for i in range(int(len(file_names) / batch_size - 1)):
# for i in range(30):
print('(batchnorm) Training on batch {}'.format(i))
X_batch, Y_batch, _ = generate_training_datum(
file_names[i * batch_size:(i + 1) * batch_size])
model.train_on_batch(X_batch, Y_batch)
if SHOW is True:
colored = colorize()
report_loss()
# color_files = random.choice(test_file_names)
if i % 10 == 0:
report_loss()
colored = colorize()
skimage.io.imsave(os.path.join(
TESTING_RESULT_DIR,
'{}_test_batchnorm_lab_'.format(i) + "00000643.jpg"),
arr=colored)
if i % 300 == 299:
model.save_weights("{}_color_batchnorm_mrcnn_lab.h5".format(i))
# ===== Store Model ===== #
# Save model
model_json = model.to_json()
with open("batchnorm_model.json", "w") as json_file:
json_file.write(model_json)
model.save_weights("auto_color_batchnorm_final_lab.h5")
text_input = Input(shape=(None,), dtype='int32', name='text')
embedded_text = layers.Embedding(text_vocabulary_size, 64)(text_input)
encoded_text = layers.LSTM(32)(embedded_text)
question_input = Input(shape=(None,), dtype='int32', name='question')
embedded_question = layers.Embedding(
question_vocabulary_size, 32)(question_input)
encoded_question = layers.LSTM(16)(embedded_question)
concatenated = layers.concatenate([encoded_text, encoded_question], axis=-1)
answer = layers.Dense(answer_vocabulary_size,
activation='softmax')(concatenated)
model = Model([text_input, question_input], answer)
model.compile(optimizer='rmsprop', loss='categorical_crossentropy',
metrics=['acc'])
# %%
num_samples = 1000
max_length = 100
text = np.random.randint(1, text_vocabulary_size,
size=(num_samples, max_length))
question = np.random.randint(
1, question_vocabulary_size, size=(num_samples, max_length))
answers = np.random.randint(1, answer_vocabulary_size, size=(num_samples,))
answers = keras.utils.to_categorical(answers, answer_vocabulary_size)
model.fit({'text': text, 'question': question},
answers, epochs=10, batch_size=128)
time_dense = PReLU(name='time_dense_prelu')(Dense(units=5,
name='time_dense')(time_bn))
inv_con = concatenate([time_dense, member_feat_dense, question_dense],
name='invite_concatenate')
inv_con_bn = BatchNormalization(name='inv_con_bn')(inv_con)
inv_dense_1 = PReLU(name='inv_dense_1_prelu')(Dense(
units=512, name='inv_dense_1')(inv_con_bn))
inv_dense_2 = PReLU()(Dense(units=128, name='inv_dense_2_prelu')(inv_dense_1))
inv_out = Dense(units=1, activation='sigmoid', name='inv_out')(inv_dense_2)
model = Model(inputs=[time_input, ques_input, member_feat_input],
outputs=inv_out)
model.compile(optimizer='adam',
loss=keras.losses.binary_crossentropy,
metrics=['acc'])
model.summary()
keras.utils.plot_model(model, './model.png', show_shapes=True)
#%%
class MGene(keras.utils.Sequence):
def __init__(self, batch_size: int, invite: pd.DataFrame):
self.batch_size = batch_size
self.invite = invite
self.length = len(invite)
self.true_len_rate = len(
invite[invite['is_answer'] == 1]) / self.length
def transform_1D_data_to_reverse_dist(
data,
new_sample_ratio=False,
return_same_sized_combined_dist=True,
bins=30,
imba_f=1.2,
visualization=True):
from keras import Input, Sequential, Model
from keras.layers import Dense
from keras.optimizers import Adam
from keras.callbacks import EarlyStopping
import matplotlib.pyplot as plt
# instead of making rare events having the same standing as frequent events, we make rare events even more common than norm
# imba factor controls the distribution of rare events > normal events
# if no_of_new_samples is not specified, it attempts to calculate the number by finding the amount of new samples
# required to fill up the remaining area of the uniform dist (think of it as the unfilled area of a rectangle'
if new_sample_ratio == 0 and new_sample_ratio != False or imba_f == 0:
return data
latent_dim = 1
feature_count = len(data[0])
enc_input = Input(shape=(feature_count, ))
encoder = Sequential()
encoder.add(Dense(100, input_shape=(feature_count, )))
encoder.add(Dense(latent_dim))
decoder = Sequential()
decoder.add(Dense(100, input_shape=(latent_dim, )))
decoder.add(Dense(feature_count))
final = Model(enc_input, decoder(encoder(enc_input)))
final.compile(optimizer=Adam(lr=1e-4), loss="mean_squared_error")
np.random.shuffle(data)
final.fit(
x=np.asarray(data),
y=np.asarray(data),
batch_size=int(len(data) / 10),
callbacks=[EarlyStopping(monitor='loss', min_delta=0.00001)],
epochs=500)
latent_values = encoder.predict(data)
if visualization:
plt.figure('Original latent values histogram')
plt.hist(latent_values, bins=bins)
if bins > len(latent_values):
bins = int(len(latent_values) / 2)
count, ranges = np.histogram(latent_values, bins=bins)
no_of_new_samples = 0
if not new_sample_ratio:
no_of_new_samples = np.sum(np.max(count) - count)
else:
no_of_new_samples = int(len(data) * new_sample_ratio)
bins_probability_table = [
np.power(x, imba_f)
for x in np.rint(max(count) - count) / max(count)
]
bins_probability_table /= np.max(bins_probability_table)
new_latent_values = []
while (True):
for i in range(len(bins_probability_table)):
bin_rng = [ranges[i], ranges[i + 1]]
bins_prob = bins_probability_table[i]
if np.random.rand() < bins_prob:
new_synth_latent = np.random.rand() * (
bin_rng[1] - bin_rng[0]) + bin_rng[0]
new_latent_values.append([new_synth_latent])
if len(new_latent_values) >= no_of_new_samples:
break
if len(new_latent_values) >= no_of_new_samples:
break
# for debugging
if len(new_latent_values) == 0:
return data
new_synth_data = decoder.predict(np.asarray(new_latent_values))
if visualization:
plt.figure('New latent values histogram')
plt.hist(np.asarray(new_latent_values), bins=bins)
plt.figure('Combined latent values histogram')
combined_latent_values = np.concatenate(
(np.asarray(new_latent_values), latent_values))
plt.hist(combined_latent_values, bins=bins)
plt.show()
# count_, ranges_ = np.histogram(new_latent_values, bins=bins)
if return_same_sized_combined_dist == True:
resampled_data = np.concatenate((data, new_synth_data))
np.random.shuffle(resampled_data)
resampled_data = resampled_data[:len(data)]
# for debugging
# debugging_latent_v = encoder.predict(resampled_data)
# plt.hist(debugging_latent_v, bins=bins)
# plt.show()
return resampled_data
return new_latent_values
def transform_1D_samples_using_DOPE(
data,
return_same_sized_combined_dist=True,
new_sample_ratio=0.3,
no_of_std=3,
visualization=False):
from keras import Input, Sequential, Model
from keras.layers import Dense
from keras.optimizers import Adam
from keras.callbacks import EarlyStopping
import matplotlib.pyplot as plt
from scipy.stats import chi
if new_sample_ratio == 0 or no_of_std == 0:
return data
latent_dim = 1
no_of_new_samples = int(len(data) * new_sample_ratio)
feature_count = len(data[0])
enc_input = Input(shape=(feature_count, ))
encoder = Sequential()
encoder.add(Dense(100, input_shape=(feature_count, )))
encoder.add(Dense(latent_dim))
decoder = Sequential()
decoder.add(Dense(100, input_shape=(latent_dim, )))
decoder.add(Dense(feature_count))
final = Model(enc_input, decoder(encoder(enc_input)))
final.compile(optimizer=Adam(lr=1e-4), loss="mean_squared_error")
np.random.shuffle(data)
final.fit(
x=np.asarray(data),
y=np.asarray(data),
batch_size=int(len(data) / 10),
callbacks=[EarlyStopping(monitor='loss', min_delta=0.00001)],
epochs=500)
latent_values = encoder.predict(data)
if visualization:
# for debugging of distribution of latent_values
plt.figure('Latent value distribution')
plt.hist(latent_values, bins=30)
plt.show()
center = np.mean(latent_values, axis=0)
std = np.std(latent_values, axis=0)
chi_std = chi.std(2, 0, np.linalg.norm(std))
# x-mean
# I have a problem with the following line, he assumes that the latent values are already gaussian
# distributed hence using it directly
dist = np.linalg.norm(latent_values - center,
axis=1) # Frobenius norm
if visualization:
# for debugging of distribution
plt.figure('L1 norm distribution')
plt.hist(dist, bins=30)
plt.show()
for i, el in enumerate(dist):
dist[i] = 0. if el > no_of_std * chi_std else dist[i]
if visualization:
# for debugging of distribution
plt.figure('L1 norm distribution after std filtering')
plt.hist(list(filter(lambda x: x > 0, dist)), bins=30)
plt.show()
threshold = sorted(dist)[int(len(dist) *
0.9)] # this is cutting too much
dist = [0. if x < threshold else x for x in dist]
if visualization:
# for debugging of distribution
plt.figure(
'L1 norm distribution after std & threshold filtering')
plt.hist(list(filter(lambda x: x > 0, dist)), bins=30)
plt.show()
dist /= np.sum(dist)
synth_latent = []
for i in range(no_of_new_samples):
# choose an ele from 1st argv, given that 1st argv has prob dist in p
choice = np.random.choice(np.arange(len(dist)), p=dist)
a = latent_values[choice]
latent_copy = np.concatenate(
(latent_values[:choice], latent_values[choice + 1:]))
latent_copy -= a
latent_copy = np.linalg.norm(latent_copy,
axis=1) # Frobenius norm
b = np.argmin(latent_copy)
if b >= choice:
b += 1
b = latent_values[b]
scale = np.random.rand()
c = scale * (a - b) + b
synth_latent.append(c)
new_latent_values = np.concatenate(
(latent_values, np.asarray(synth_latent)))
new_data = decoder.predict(np.asarray(synth_latent))
if return_same_sized_combined_dist:
resampled_data = np.concatenate((data, new_data))
np.random.shuffle(resampled_data)
return resampled_data[:len(data)]
return new_data
class CharacterTagger:
"""
A class for character-based neural morphological tagger
"""
def __init__(self,
reverse=False,
word_rnn="cnn",
min_char_count=1,
char_embeddings_size=16,
char_conv_layers=1,
char_window_size=5,
char_filters=None,
char_filter_multiple=25,
char_highway_layers=1,
conv_dropout=0.0,
highway_dropout=0.0,
intermediate_dropout=0.0,
lstm_dropout=0.0,
word_lstm_layers=1,
word_lstm_units=128,
word_dropout=0.0,
regularizer=None,
batch_size=16,
validation_split=0.2,
nepochs=25,
min_prob=0.01,
max_diff=2.0,
callbacks=None,
verbose=1):
self.reverse = reverse
self.word_rnn = word_rnn
self.min_char_count = min_char_count
self.char_embeddings_size = char_embeddings_size
self.char_conv_layers = char_conv_layers
self.char_window_size = char_window_size
self.char_filters = char_filters
self.char_filter_multiple = char_filter_multiple
self.char_highway_layers = char_highway_layers
self.conv_dropout = conv_dropout
self.highway_dropout = highway_dropout
self.intermediate_dropout = intermediate_dropout
self.word_lstm_layers = word_lstm_layers
self.word_lstm_units = word_lstm_units
self.lstm_dropout = lstm_dropout
self.word_dropout = word_dropout
self.regularizer = regularizer
self.batch_size = batch_size
self.validation_split = validation_split
self.nepochs = nepochs
self.min_prob = min_prob
self.max_diff = max_diff
self.callbacks = callbacks
self.verbose = verbose
self.initialize()
def initialize(self):
if isinstance(self.char_window_size, int):
self.char_window_size = [self.char_window_size]
if self.char_filters is None or isinstance(self.char_filters, int):
self.char_filters = [self.char_filters] * len(
self.char_window_size)
if len(self.char_window_size) != len(self.char_filters):
raise ValueError(
"There should be the same number of window sizes and filter sizes"
)
if isinstance(self.word_lstm_units, int):
self.word_lstm_units = [self.word_lstm_units
] * self.word_lstm_layers
if len(self.word_lstm_units) != self.word_lstm_layers:
raise ValueError(
"There should be the same number of lstm layer units and lstm layers"
)
if self.regularizer is not None:
self.regularizer = kreg.l2(self.regularizer)
def to_json(self, outfile, model_file, lm_file=None):
info = dict()
if lm_file is not None:
info["lm_file"] = lm_file
# model_file = os.path.abspath(model_file)
for (attr, val) in inspect.getmembers(self):
if not (attr.startswith("__") or inspect.ismethod(val)
or isinstance(getattr(CharacterTagger, attr, None),
property) or isinstance(val, np.ndarray)
or isinstance(val, Vocabulary) or attr.isupper()
or attr in ["callbacks", "model_", "regularizer"]):
info[attr] = val
elif isinstance(val, Vocabulary):
info[attr] = val.jsonize()
elif isinstance(val, np.ndarray):
val = val.tolist()
info[attr] = val
elif attr == "model_":
info["dump_file"] = model_file
self.model_.save_weights(model_file)
elif attr == "callbacks":
for callback in val:
if isinstance(callback, EarlyStopping):
info["early_stopping_callback"] = {
"patience": callback.patience,
"monitor": callback.monitor
}
elif isinstance(callback, ModelCheckpoint):
info["model_checkpoint_callback"] =\
{key: getattr(callback, key) for key in ["monitor", "filepath"]}
elif isinstance(callback, ReduceLROnPlateau):
info["LR_callback"] =\
{key: getattr(callback, key) for key in
["monitor", "factor", "patience", "cooldown", "epsilon"]}
elif attr.endswith("regularizer"):
if val is not None:
info[attr] = float(val.l2)
with open(outfile, "w", encoding="utf8") as fout:
json.dump(info, fout)
@property
def symbols_number_(self):
return self.symbols_.symbols_number_
@property
def tags_number_(self):
return self.tags_.symbols_number_
def transform(self,
data,
labels=None,
pad=True,
return_indexes=True,
buckets_number=None,
bucket_size=None,
join_buckets=True):
lengths = [len(x) + 2 for x in data]
if pad:
indexes, level_lengths = make_bucket_indexes(
lengths,
buckets_number=buckets_number,
bucket_size=bucket_size,
join_buckets=join_buckets)
else:
indexes = [[i] for i in range(len(data))]
level_lengths = lengths
X = [None] * len(data)
for bucket_indexes, bucket_length in zip(indexes, level_lengths):
for i in bucket_indexes:
sent = data[i] if not self.reverse else data[i][::-1]
X[i] = [
self._make_sent_vector(sent, bucket_length=bucket_length)
]
if labels is not None:
tags = labels[i] if not self.reverse else labels[i][::-1]
X[i].append(
self._make_tags_vector(tags,
bucket_length=bucket_length))
if return_indexes:
return X, indexes
else:
return X
def _make_sent_vector(self, sent, bucket_length=None):
if bucket_length is None:
bucket_length = len(sent)
answer = np.zeros(shape=(bucket_length, MAX_WORD_LENGTH + 2),
dtype=np.int32)
for i, word in enumerate(sent):
answer[i, 0] = BEGIN
m = min(len(word), MAX_WORD_LENGTH)
for j, x in enumerate(word[-m:]):
answer[i, j + 1] = self.symbols_.toidx(x)
answer[i, m + 1] = END
answer[i, m + 2:] = PAD
return answer
def _make_tags_vector(self, tags, bucket_length=None, func=None):
m = len(tags)
if bucket_length is None:
bucket_length = m
answer = np.zeros(shape=(bucket_length, ), dtype=np.int32)
for i, tag in enumerate(tags):
answer[i] = self.tags_.toidx(tag) if func is None else func(tag)
return answer
def train(self,
data,
labels,
dev_data=None,
dev_labels=None,
symbol_vocabulary_file=None,
tags_vocabulary_file=None,
lm_file=None,
model_file=None,
save_file=None):
"""
Trains the tagger on data :data: with labels :labels:
data: list of lists of sequences, a list of sentences
labels: list of lists of strs,
a list of sequences of tags, each tag is a feature-value structure
:return:
"""
if symbol_vocabulary_file is None:
self.symbols_ = Vocabulary(
character=True, min_count=self.min_char_count).train(data)
else:
self.symbols_ = vocabulary_from_json(symbol_vocabulary_file,
use_features=False)
if tags_vocabulary_file is None:
self.tags_ = FeatureVocabulary(character=False).train(labels)
else:
with open(tags_vocabulary_file, "r", encoding="utf8") as fin:
tags_info = json.load(fin)
self.tags_ = vocabulary_from_json(tags_info, use_features=True)
if self.verbose > 0:
print("{} characters, {} tags".format(self.symbols_number_,
self.tags_number_))
X_train, indexes_by_buckets = self.transform(data,
labels,
buckets_number=10)
if dev_data is not None:
X_dev, dev_indexes_by_buckets =\
self.transform(dev_data, dev_labels, bucket_size=BUCKET_SIZE)
else:
X_dev, dev_indexes_by_buckets = [None] * 2
self.build()
if save_file is not None and model_file is not None:
self.to_json(save_file, model_file, lm_file)
self._train_on_data(X_train,
indexes_by_buckets,
X_dev,
dev_indexes_by_buckets,
model_file=model_file)
return self
def _train_on_data(self,
X,
indexes_by_buckets,
X_dev=None,
dev_indexes_by_buckets=None,
model_file=None):
if X_dev is None:
X_dev, dev_indexes_by_buckets = X, []
validation_split = self.validation_split
else:
validation_split = 0.0
train_indexes_by_buckets = []
for curr_indexes in indexes_by_buckets:
np.random.shuffle(curr_indexes)
if validation_split != 0.0:
train_bucket_size = int(
(1.0 - self.validation_split) * len(curr_indexes))
train_indexes_by_buckets.append(
curr_indexes[:train_bucket_size])
dev_indexes_by_buckets.append(curr_indexes[train_bucket_size:])
else:
train_indexes_by_buckets.append(curr_indexes)
if model_file is not None:
callback = ModelCheckpoint(model_file,
monitor="val_acc",
save_weights_only=True,
save_best_only=True)
if self.callbacks is not None:
self.callbacks.append(callback)
else:
self.callbacks = [callback]
train_steps = sum((1 + (len(x) - 1) // self.batch_size)
for x in train_indexes_by_buckets)
dev_steps = len(dev_indexes_by_buckets)
train_gen = generate_data(X,
train_indexes_by_buckets,
self.tags_number_,
self.batch_size,
use_last=False)
dev_gen = generate_data(X_dev,
dev_indexes_by_buckets,
self.tags_number_,
use_last=False,
shuffle=False)
self.model_.fit_generator(train_gen,
steps_per_epoch=train_steps,
epochs=self.nepochs,
callbacks=self.callbacks,
validation_data=dev_gen,
validation_steps=dev_steps,
verbose=1)
if model_file is not None:
self.model_.load_weights(model_file)
return self
def predict(self, data, labels=None, return_probs=False):
X_test, indexes_by_buckets =\
self.transform(data, labels=labels, bucket_size=BUCKET_SIZE)
answer, probs = [None] * len(data), [None] * len(data)
for k, (X_curr, bucket_indexes) in enumerate(
zip(X_test[::-1], indexes_by_buckets[::-1])):
X_curr = [
np.array([X_test[i][j] for i in bucket_indexes])
for j in range(len(X_test[0]) - int(labels is not None))
]
bucket_probs = self.model_.predict(X_curr, batch_size=256)
bucket_labels = np.argmax(bucket_probs, axis=-1)
for curr_labels, curr_probs, index in\
zip(bucket_labels, bucket_probs, bucket_indexes):
curr_labels = curr_labels[:len(data[index])]
curr_labels = [
self.tags_.symbols_[label] for label in curr_labels
]
answer[index], probs[
index] = curr_labels, curr_probs[:len(data[index])]
return (answer, probs) if return_probs else answer
def score(self, data, labels):
X_test, indexes_by_buckets = self.transform(data,
labels,
bucket_size=BUCKET_SIZE)
probs = [None] * len(data)
for k, (X_curr, bucket_indexes) in enumerate(
zip(X_test[::-1], indexes_by_buckets[::-1])):
X_curr = [
np.array([X_test[i][j] for i in bucket_indexes])
for j in range(len(X_test[0]) - 1)
]
y_curr = [np.array(X_test[i][-1]) for i in bucket_indexes]
bucket_probs = self.model_.predict(X_curr, batch_size=256)
for curr_labels, curr_probs, index in zip(y_curr, bucket_probs,
bucket_indexes):
L = len(data[index])
probs[index] = curr_probs[np.arange(L), curr_labels[:L]]
return probs
def build(self):
word_inputs = kl.Input(shape=(None, MAX_WORD_LENGTH + 2),
dtype="int32")
inputs = [word_inputs]
word_outputs = self.build_word_cnn(word_inputs)
outputs, lstm_outputs = self.build_basic_network(word_outputs)
compile_args = {
"optimizer": ko.nadam(lr=0.002, clipnorm=5.0),
"loss": "categorical_crossentropy",
"metrics": ["accuracy"]
}
self.model_ = Model(inputs, outputs)
self.model_.compile(**compile_args)
if self.verbose > 0:
print(self.model_.summary())
return self
def build_word_cnn(self, inputs):
# inputs = kl.Input(shape=(MAX_WORD_LENGTH,), dtype="int32")
inputs = kl.Lambda(kb.one_hot,
arguments={"num_classes": self.symbols_number_},
output_shape=lambda x: tuple(x) +
(self.symbols_number_, ))(inputs)
char_embeddings = kl.Dense(self.char_embeddings_size,
use_bias=False)(inputs)
conv_outputs = []
self.char_output_dim_ = 0
for window_size, filters_number in zip(self.char_window_size,
self.char_filters):
curr_output = char_embeddings
curr_filters_number = (min(self.char_filter_multiple *
window_size, 200) if
filters_number is None else filters_number)
for _ in range(self.char_conv_layers - 1):
curr_output = kl.Conv2D(
curr_filters_number, (1, window_size),
padding="same",
activation="relu",
data_format="channels_last")(curr_output)
if self.conv_dropout > 0.0:
curr_output = kl.Dropout(self.conv_dropout)(curr_output)
curr_output = kl.Conv2D(curr_filters_number, (1, window_size),
padding="same",
activation="relu",
data_format="channels_last")(curr_output)
conv_outputs.append(curr_output)
self.char_output_dim_ += curr_filters_number
if len(conv_outputs) > 1:
conv_output = kl.Concatenate(axis=-1)(conv_outputs)
else:
conv_output = conv_outputs[0]
highway_input = kl.Lambda(kb.max, arguments={"axis": -2})(conv_output)
if self.intermediate_dropout > 0.0:
highway_input = kl.Dropout(
self.intermediate_dropout)(highway_input)
for i in range(self.char_highway_layers - 1):
highway_input = Highway(activation="relu")(highway_input)
if self.highway_dropout > 0.0:
highway_input = kl.Dropout(self.highway_dropout)(highway_input)
highway_output = Highway(activation="relu")(highway_input)
return highway_output
def build_basic_network(self, word_outputs):
"""
Creates the basic network architecture,
transforming word embeddings to intermediate outputs
"""
if self.word_dropout > 0.0:
lstm_outputs = kl.Dropout(self.word_dropout)(word_outputs)
else:
lstm_outputs = word_outputs
for j in range(self.word_lstm_layers - 1):
lstm_outputs = kl.Bidirectional(
kl.LSTM(self.word_lstm_units[j],
return_sequences=True,
dropout=self.lstm_dropout))(lstm_outputs)
lstm_outputs = kl.Bidirectional(
kl.LSTM(self.word_lstm_units[-1],
return_sequences=True,
dropout=self.lstm_dropout))(lstm_outputs)
pre_outputs = kl.TimeDistributed(kl.Dense(
self.tags_number_,
activation="softmax",
activity_regularizer=self.regularizer),
name="p")(lstm_outputs)
return pre_outputs, lstm_outputs
# Flatten feature map to a 1-dim tensor
x = layers.Flatten()(x)
# Create a fully connected layer with ReLU activation and 512 hidden units
x = layers.Dense(512, activation='relu')(x)
# Add a dropout rate of 0.5
x = layers.Dropout(0.5)(x)
# Create output layer with a single node and sigmoid activation
output = layers.Dense(1, activation='sigmoid')(x)
# Configure and compile the model
model = Model(img_input, output)
model.compile(loss='binary_crossentropy',
optimizer=RMSprop(lr=0.001),
metrics=['acc'])
history = model.fit_generator(train_generator,
steps_per_epoch=100,
epochs=30,
validation_data=validation_generator,
validation_steps=50,
verbose=2)
# Retrieve a list of accuracy results on training and test data
# sets for each training epoch
acc = history.history['acc']
val_acc = history.history['val_acc']
# Retrieve a list of list results on training and test data
# Make both of the discriminator networks non-trainable
discriminatorA.trainable = False
discriminatorB.trainable = False
probsA = discriminatorA(generatedA)
probsB = discriminatorB(generatedB)
adversarial_model = Model(inputs=[inputA, inputB],
outputs=[
probsA, probsB, reconstructedA,
reconstructedB, generatedAId,
generatedBId
])
adversarial_model.compile(
loss=['mse', 'mse', 'mae', 'mae', 'mae', 'mae'],
loss_weights=[1, 1, 10.0, 10.0, 1.0, 1.0],
optimizer=common_optimizer)
tensorboard = TensorBoard(log_dir="logs/{}".format(time.time()),
write_images=True,
write_grads=True,
write_graph=True)
tensorboard.set_model(generatorAToB)
tensorboard.set_model(generatorBToA)
tensorboard.set_model(discriminatorA)
tensorboard.set_model(discriminatorB)
real_labels = np.ones((batch_size, 7, 7, 1))
fake_labels = np.zeros((batch_size, 7, 7, 1))
for epoch in range(epochs):
from keras import models
from keras.layers import Conv2D, Flatten, MaxPooling2D, Dense, GlobalAveragePooling2D, Dropout
from keras import layers
from keras import Model
import logging
from keras.applications.inception_v3 import InceptionV3
X_size = 75
Y_size = 75
base_model = InceptionV3(include_top=False,
input_shape=(X_size, Y_size, 3),
classes=52)
x = base_model.output
x = GlobalAveragePooling2D()(x)
x = Dropout(0.7)(x)
predictions = Dense(52, activation='softmax')(x)
model = Model(inputs=base_model.input, outputs=predictions)
datagen = ImageDataGenerator(rescale=1. / 255)
train_generator = datagen.flow_from_directory('./trafficSignsHW/trainFULL',
target_size=(X_size, Y_size),
batch_size=32,
class_mode='categorical')
model.compile(keras.optimizers.Adam(),
'categorical_crossentropy',
metrics=['accuracy'])
model.fit_generator(train_generator, steps_per_epoch=20, epochs=32)
model.save("my_model.h5")
def build(self,
sentence_length,
word_length,
target_label_dims,
word_vocab,
word_vocab_size,
char_vocab_size,
word_embedding_dims=100,
char_embedding_dims=25,
word_lstm_dims=25,
tagger_lstm_dims=100,
tagger_fc_dims=100,
dropout=0.2,
external_embedding_model=None):
"""
Build a NERCRF model
Args:
sentence_length (int): max sentence length
word_length (int): max word length in characters
target_label_dims (int): number of entity labels (for classification)
word_vocab (dict): word to int dictionary
word_vocab_size (int): word vocabulary size
char_vocab_size (int): character vocabulary size
word_embedding_dims (int): word embedding dimensions
char_embedding_dims (int): character embedding dimensions
word_lstm_dims (int): character LSTM feature extractor output dimensions
tagger_lstm_dims (int): word tagger LSTM output dimensions
tagger_fc_dims (int): output fully-connected layer size
dropout (float): dropout rate
external_embedding_model (str): path to external word embedding model
"""
# build word input
words_input = Input(shape=(sentence_length,), name='words_input')
if external_embedding_model is not None:
# load and prepare external word embedding
external_emb, ext_emb_size = load_word_embeddings(external_embedding_model)
embedding_matrix = np.zeros((word_vocab_size, ext_emb_size))
for word, i in word_vocab.items():
embedding_vector = external_emb.get(word.lower())
if embedding_vector is not None:
# words not found in embedding index will be all-zeros.
embedding_matrix[i] = embedding_vector
# load pre-trained word embeddings into an Embedding layer
# note that we set trainable = False so as to keep the embeddings fixed
embedding_layer = Embedding(word_vocab_size,
ext_emb_size,
weights=[embedding_matrix],
input_length=sentence_length,
trainable=False)
else:
# learn embeddings ourselves
embedding_layer = Embedding(word_vocab_size, word_embedding_dims,
input_length=sentence_length)
word_embeddings = embedding_layer(words_input)
word_embeddings = Dropout(dropout)(word_embeddings)
# create word character embeddings
word_chars_input = Input(shape=(sentence_length, word_length), name='word_chars_input')
char_embedding_layer = Embedding(char_vocab_size, char_embedding_dims,
input_length=word_length)
char_embeddings = TimeDistributed(char_embedding_layer)(word_chars_input)
char_embeddings = TimeDistributed(Bidirectional(LSTM(word_lstm_dims)))(char_embeddings)
char_embeddings = Dropout(dropout)(char_embeddings)
# create the final feature vectors
features = concatenate([word_embeddings, char_embeddings], axis=-1)
# encode using a bi-lstm
bilstm = Bidirectional(LSTM(tagger_lstm_dims, return_sequences=True))(features)
bilstm = Dropout(dropout)(bilstm)
after_lstm_hidden = Dense(tagger_fc_dims)(bilstm)
# classify the dense vectors
crf = CRF(target_label_dims, sparse_target=False)
predictions = crf(after_lstm_hidden)
# compile the model
model = Model(inputs=[words_input, word_chars_input], outputs=predictions)
model.compile(loss=crf.loss_function,
optimizer='adam',
metrics=[crf.accuracy])
self.model = model
def train(hps, epochs, save_interval=200):
half_batch = int(hps.batch_size / 2)
dataset, shape = data.load_dataset(hps)
# loss values for further plotting
model = mb.CapsuleGANModel(hps, shape)
discriminator = model.build_discriminator()
generator = model.build_generator()
discriminator.compile(loss='binary_crossentropy',
optimizer=Adam(hps.learning_rate, hps.beta_1,
hps.beta_2, hps.epsilon),
metrics=['accuracy'])
generator.compile(loss='binary_crossentropy',
optimizer=Adam(hps.learning_rate, hps.beta_1, hps.beta_2,
hps.epsilon))
z = Input(shape=(100, ))
img = generator(z)
discriminator.trainable = False
valid = discriminator(img)
combined = Model(z, valid)
combined.compile(loss='binary_crossentropy',
optimizer=Adam(hps.learning_rate, hps.beta_1, hps.beta_2,
hps.epsilon))
for epoch in range(epochs):
# ---------------------
# Train Discriminator
# ---------------------
# select a random half batch of images
idx = np.random.randint(0, dataset.shape[0], half_batch)
imgs = dataset[idx]
noise = np.random.normal(0, 1, (half_batch, 100))
# generate a half batch of new images
gen_imgs = generator.predict(noise)
# train the discriminator by feeding both real and fake (generated) images one by one
d_loss_real = discriminator.train_on_batch(
imgs,
np.ones((half_batch, 1)) * 0.9) # 0.9 for label smoothing
d_loss_fake = discriminator.train_on_batch(gen_imgs,
np.zeros((half_batch, 1)))
d_loss = 0.5 * np.add(d_loss_real, d_loss_fake)
# ---------------------
# Train Generator
# ---------------------
noise = np.random.normal(0, 1, (hps.batch_size, 100))
# the generator wants the discriminator to label the generated samples
# as valid (ones)
valid_y = np.array([1] * hps.batch_size)
# train the generator
g_loss = combined.train_on_batch(noise, np.ones((hps.batch_size, 1)))
# Plot the progress
print("%d [D loss: %f, acc.: %.2f%%] [G loss: %f]" %
(epoch, d_loss[0], 100 * d_loss[1], g_loss))
model.D_L_REAL.append(d_loss_real)
model.D_L_FAKE.append(d_loss_fake)
model.D_L.append(d_loss)
model.D_ACC.append(d_loss[1])
model.G_L.append(g_loss)
# if at save interval => save generated image samples
if epoch % (5 * save_interval) == 0:
su.save_imgs(hps.module, generator, epoch, hps)
if epoch % (10 * save_interval) == 0:
generator.save(
os.path.join(hps.model_dir,
hps.module + '_gen_model_{}.h5'.format(epoch)))
discriminator.save(
os.path.join(hps.model_dir,
hps.module + '_dis_model_{}.h5'.format(epoch)))
# if epoch % (15*save_interval) == 0:
# # joblib.dump(model, "model_{}.pkl".format(epoch))
# with open("model_{}.json".format(epoch), 'w') as f:
# ujson.dump(model, f)
# f.close()
plt.plot(model.D_L)
plt.title('Discriminator results')
plt.xlabel('Epochs')
plt.ylabel('Discriminator Loss (blue), Discriminator Accuracy (orange)')
plt.legend(['Discriminator Loss', 'Discriminator Accuracy'])
su.save_fig("{}_DL".format(hps.module))
plt.plot(model.G_L)
plt.title('Generator results')
plt.xlabel('Epochs')
plt.ylabel('Generator Loss (blue)')
plt.legend('Generator Loss')
su.save_fig("{}_GL".format(hps.module))
def build_model(vectors, shape, settings):
max_length, nr_hidden, nr_class = shape
input1 = layers.Input(shape=(max_length,), dtype="int32", name="words1")
input2 = layers.Input(shape=(max_length,), dtype="int32", name="words2")
# embeddings (projected)
embed = create_embedding(vectors, max_length, nr_hidden)
a = embed(input1)
b = embed(input2)
# step 1: attend
F = create_feedforward(nr_hidden)
att_weights = layers.dot([F(a), F(b)], axes=-1)
G = create_feedforward(nr_hidden)
if settings["entail_dir"] == "both":
norm_weights_a = layers.Lambda(normalizer(1))(att_weights)
norm_weights_b = layers.Lambda(normalizer(2))(att_weights)
alpha = layers.dot([norm_weights_a, a], axes=1)
beta = layers.dot([norm_weights_b, b], axes=1)
# step 2: compare
comp1 = layers.concatenate([a, beta])
comp2 = layers.concatenate([b, alpha])
v1 = layers.TimeDistributed(G)(comp1)
v2 = layers.TimeDistributed(G)(comp2)
# step 3: aggregate
v1_sum = layers.Lambda(sum_word)(v1)
v2_sum = layers.Lambda(sum_word)(v2)
concat = layers.concatenate([v1_sum, v2_sum])
elif settings["entail_dir"] == "left":
norm_weights_a = layers.Lambda(normalizer(1))(att_weights)
alpha = layers.dot([norm_weights_a, a], axes=1)
comp2 = layers.concatenate([b, alpha])
v2 = layers.TimeDistributed(G)(comp2)
v2_sum = layers.Lambda(sum_word)(v2)
concat = v2_sum
else:
norm_weights_b = layers.Lambda(normalizer(2))(att_weights)
beta = layers.dot([norm_weights_b, b], axes=1)
comp1 = layers.concatenate([a, beta])
v1 = layers.TimeDistributed(G)(comp1)
v1_sum = layers.Lambda(sum_word)(v1)
concat = v1_sum
H = create_feedforward(nr_hidden)
out = H(concat)
out = layers.Dense(nr_class, activation="softmax")(out)
model = Model([input1, input2], out)
model.compile(
optimizer=optimizers.Adam(lr=settings["lr"]),
loss="categorical_crossentropy",
metrics=["accuracy"],
)
return model
def construct_model(self):
"""
Construct the :math:`1`-st order and :math:`0`-th order models, which are used to approximate the
:math:`U_1(x, C(x))` and the :math:`U_0(x)` utilities respectively. For each pair of objects in
:math:`x_i, x_j \in Q` :math:`U_1(x, C(x))` we construct :class:`CmpNetCore` with weight sharing to
approximate a pairwise-matrix. A pairwise matrix with index (i,j) corresponds to the :math:`U_1(x_i,x_j)`
is a measure of how favorable it is to choose :math:`x_i` over :math:`x_j`. Using this matrix we calculate
the borda score for each object to calculate :math:`U_1(x, C(x))`. For `0`-th order model we construct
:math:`\lvert Q \lvert` sequential networks whose weights are shared to evaluate the :math:`U_0(x)` for
each object in the query set :math:`Q`. The output mode is using linear activation.
Returns
-------
model: keras :class:`Model`
Neural network to learn the FETA utility score
"""
def create_input_lambda(i):
return Lambda(lambda x: x[:, i])
if self._use_zeroth_model:
self.logger.debug('Create 0th order model')
zeroth_order_outputs = []
inputs = []
for i in range(self.n_objects):
x = create_input_lambda(i)(self.input_layer)
inputs.append(x)
for hidden in self.hidden_layers_zeroth:
x = hidden(x)
zeroth_order_outputs.append(self.output_node_zeroth(x))
zeroth_order_scores = concatenate(zeroth_order_outputs)
self.logger.debug('0th order model finished')
self.logger.debug('Create 1st order model')
outputs = [list() for _ in range(self.n_objects)]
for i, j in combinations(range(self.n_objects), 2):
if self._use_zeroth_model:
x1 = inputs[i]
x2 = inputs[j]
else:
x1 = create_input_lambda(i)(self.input_layer)
x2 = create_input_lambda(j)(self.input_layer)
x1x2 = concatenate([x1, x2])
x2x1 = concatenate([x2, x1])
for hidden in self.hidden_layers:
x1x2 = hidden(x1x2)
x2x1 = hidden(x2x1)
merged_left = concatenate([x1x2, x2x1])
merged_right = concatenate([x2x1, x1x2])
n_g = self.output_node(merged_left)
n_l = self.output_node(merged_right)
outputs[i].append(n_g)
outputs[j].append(n_l)
# convert rows of pairwise matrix to keras layers:
outputs = [concatenate(x) for x in outputs]
# compute utility scores:
sum_func = lambda s: K.mean(s, axis=1, keepdims=True)
scores = [Lambda(sum_func)(x) for x in outputs]
scores = concatenate(scores)
self.logger.debug('1st order model finished')
if self._use_zeroth_model:
scores = add([scores, zeroth_order_scores])
model = Model(inputs=self.input_layer, outputs=scores)
self.logger.debug('Compiling complete model...')
model.compile(loss=self.loss_function,
optimizer=self.optimizer,
metrics=self.metrics)
return model
vocabulary_size = 50000
num_income_groups = 10
posts_input = Input(shape=(None,), dtype='int32', name='posts')
embedded_posts = layers.Embedding(vocabulary_size, 256)(posts_input)
x = layers.Conv1D(128, 5, activation='relu')(embedded_posts)
x = layers.MaxPooling1D(5)(x)
x = layers.Conv1D(256, 5, activation='relu')(x)
x = layers.Conv1D(256, 5, activation='relu')(x)
x = layers.MaxPooling1D(5)(x)
x = layers.Conv1D(256, 5, activation='relu')(x)
x = layers.Conv1D(256, 5, activation='relu')(x)
x = layers.GlobalMaxPooling1D()(x)
x = layers.Dense(128, activation='relu')(x)
age_prediction = layers.Dense(1, name='age')(x)
income_prediction = layers.Dense(
num_income_groups, activation='softmax', name='income')(x)
gender_prediction = layers.Dense(1, activation='sigmoid', name='gender')(x)
model = Model(posts_input, [age_prediction,
income_prediction, gender_prediction])
model.compile(optimizer='rmsprop',
loss={'age': 'mse',
'income': 'categorical_crossentropy',
'gender': 'binary_crossentropy'},
loss_weights={'age': 0.25,
'income': 1.0,
'gender': 10.0})
X = np.random.randint(10, size=(n_samples, dx, dy))
y_true = np.ones((n_samples, dx, dout))
# X[2, 0] = mask_value
# X[3, 1] = mask_value
sample_weight = np.ones_like(y_true)
# sample_weight[2, 0] = 0
# sample_weight[3, 1] = 0
sample_weight[0, 0] = 0
inp = Input(shape=(dx, dy))
dense = TimeDistributed(Dense(dout))(inp)
model = Model(inputs=inp, outputs=dense)
model.summary()
model.compile(optimizer="rmsprop", loss="mae", sample_weight_mode="temporal")
set_model_weights_to_unity(model)
y_pred = model.predict(X, verbose=0)
unmasked_loss = mae(y_true, y_pred, mask=False)
masked_loss = mae(y_true, y_pred, mask=True)
weighted_loss = mae(y_true, y_pred, mask=False, weights=sample_weight)
keras_loss = model.evaluate(X, y_true, verbose=0)
keras_loss_weighted = model.evaluate(X,
y_true,
sample_weight=sample_weight[..., 0],
verbose=0)
print(f"unmasked loss: {unmasked_loss}")
print(f"masked loss: {masked_loss}")
print(f"weighted loss: {weighted_loss}")
print(f"evaluate with Keras: {keras_loss}")
model = Dropout(droprate)(
model
) # To forget ordrop the few pixels from the layer to avoid the learnign the noise of the parameter.
#Fully connected final layer
model = Dense(num_classes)(
model) # To connect all the layers as fully connected layers.
model = Activation('softmax')(
model
) # As its a multi class classfication, softmax is used. This will add the model probability output from each output nodes to 1.
val = Model(inputs, model)
#compile model using accuracy to measure model performance
val.compile(loss=keras.losses.categorical_crossentropy,
optimizer=keras.optimizers.RMSprop(),
metrics=['accuracy'])
#describe the layers
val.summary()
# define path to save model
model_path = Path + 'fm_cnn_BN16.h5'
# prepare callbacks
callbacks = [
EarlyStopping(monitor='val_acc', patience=10, mode='max', verbose=1),
ModelCheckpoint(model_path,
monitor='val_acc',
save_best_only=True,
mode='max',
model = layers.Dense(128)(model)
model = layers.Dense(64)(model)
model = layers.Dense(4)(model)
output = layers.Activation('softmax')(model)
model = Model(input,output)
model.summary()
callback_list = [keras.callbacks.EarlyStopping(monitor = 'val_acc', patience = 5),
keras.callbacks.ModelCheckpoint(filepath='ResNet18.h5', monitor = 'val_loss', save_best_only = True)]
model.compile(loss = 'sparse_categorical_crossentropy',
optimizer = 'adam',
metrics = ['acc'])
import time # 훈련할 때마다 time을 가져와서 초기화를 시켜줘야 합니다!
start = time.time()
history = model.fit(train_image, train_label, epochs = 100, callbacks=callback_list,validation_data = (test_image,test_label))
time = time.time() - start
print("테스트 시 소요 시간(초) : {}".format(time))
print("전체 파라미터 수 : {}".format(sum([arr.flatten().shape[0] for arr in model.get_weights()])))
# 모델 훈련이 잘 되었는지 그래프로 확입힙니다.
acc = history.history['acc']
val_acc = history.history['val_acc']
loss = history.history['loss']
val_loss = history.history['val_loss']
crf = CRF(len(label.index) + 1, learn_mode='marginal')(gru_kata)
preds = Dense(len(label.index) + 1, activation='softmax')(gru_kata)
print "Model Choice:"
model_choice = 1 # input('Enter 1 for CRF or 2 for Dense layer: ')
model = Model(inputs=[sequence_input, sequence_input_c], outputs=[crf])
if model_choice == 2:
model = Model(inputs=[sequence_input, sequence_input_c], outputs=[preds])
optimizer = 'adam' # raw_input('Enter optimizer (default rmsprop): ')
loss = 'binary_crossentropy' # raw_input('Enter loss function (default categorical_crossentropy): ')
model.summary()
model.compile(loss=loss,
optimizer=optimizer,
metrics=['acc'])
load_m = False
"""
Training
"""
epoch = input('Enter number of epochs: ')
batch = input('Enter number of batch size: ')
model.fit([np.array(x_train.padded), np.array(x_train_char)],
[np.array(y_encoded)],
epochs=epoch, batch_size=batch)
"""
Converting text data to int using index
true_positives = K.cast(true_ones, K.floatx())
true_positive_count = K.sum(true_positives)
label_positive_count = K.sum(y_true)
recall = true_positive_count / label_positive_count
return recall
def precision_m(y_true, y_pred):
y_true = K.round(y_true)
y_pred = K.round(y_pred)
pair_sum = tf.add(y_true, y_pred)
true_ones = K.equal(pair_sum, 2.)
true_positives = K.cast(true_ones, K.floatx())
true_positive_count = K.sum(true_positives)
pred_positive_count = K.sum(y_pred)
precision = true_positive_count / pred_positive_count
return precision
def f1_m(y_true, y_pred):
precision = precision_m(y_true, y_pred)
recall = recall_m(y_true, y_pred)
return 2 * ((precision * recall) / (precision + recall))
# Another way to define your optimizer
adam = Adam(lr=0.001)
# We add metrics to get more results you want to see
model = Model(inputs="your inputs")
model.compile(optimizer=adam, loss="mean_squared_error", metrics=['categorical_accuracy', recall_m, precision_m, f1_m])
D_out_11, D_out_12, D_out_13, D_out_14, D_out_21, D_out_22, D_out_23,
D_out_24, D_out_31, D_out_32, D_out_33, D_out_34, D_out_41, D_out_42,
D_out_43, D_out_44
])
GAN = Model(inputs=[G_input],
outputs=[generated_image, GAN_output],
name="GAN")
GAN_loss = [laplacian_loss, 'binary_crossentropy']
opt_GAN = Adam(lr=lr_schedule(0, G_inital_lr, G_decay_factor, G_decay_period),
beta_1=0.9,
beta_2=0.999,
epsilon=1e-08)
loss_weights = [1, 0.005]
GAN.compile(loss=GAN_loss,
loss_weights=loss_weights,
optimizer=opt_GAN,
metrics={'model_1': mae_on_first_channel})
GAN.summary()
# training start here
real_val = 1.0
fake_val = 0.0
# can load pretrained models here, not necessary
# D.load_weights('save/pre_D.hdf5')
G.load_weights('save/formalin_g_G_500.hdf5')
for iteration in range(0, num_iters, 1):
# train D until D can distinguish real and generated images
lr_D = lr_schedule(iteration, D_inital_lr, D_decay_factor, D_decay_period)
K.set_value(D.optimizer.lr, lr_D)
embedding = Embedding(vocab_size,
embedding_vector_size,
input_length=1,
name='embedding',
weights=model_mat_skip_gram)
target = embedding(input_target)
target = Reshape((embedding_vector_size, 1))(target)
context = embedding(input_context)
context = Reshape((embedding_vector_size, 1))(context)
# setup a cosine similarity operation which will be output in a secondary model
# similarity = merge([target, context], mode='cos', dot_axes=0)
similarity = dot([target, context], axes=1, normalize=True)
# now perform the dot product operation to get a similarity measure
dot_product = dot([target, context], axes=1)
dot_product = Reshape((1, ))(dot_product)
# add the sigmoid output layer
output = Dense(1, activation='sigmoid')(dot_product)
# create the primary training model
model = Model(input=[input_target, input_context], output=output)
model.compile(loss='binary_crossentropy', optimizer='rmsprop')
model.summary()
# create a secondary validation model to run our similarity checks during training
validation_model = Model(input=[input_target, input_context],
output=similarity)
callb = SimilarityCallback(t)
callb.run_sim()
xtest = sequence.pad_sequences(xtest, maxlen=maxima_longitud)
#Creacion de modelo
entrada = Input(shape=(maxima_longitud, ))
x = Embedding(maximas_caracteristicas,
tamano_embedding)(entrada) #Capa especial para texto.
x = LSTM(tamano_embedding, return_sequences=True, activation='relu')(
x) #returns_sequences devuelde los estados obtenidos por embedding.
x = Flatten()(x) #Llevar a una dimension
x = Dense(1,
activation="sigmoid",
kernel_initializer='zeros',
bias_initializer='zeros')(x)
modelo = Model(inputs=entrada, outputs=x)
modelo.compile(loss='binary_crossentropy',
optimizer='adam',
metrics=['binary_accuracy'])
modelo.summary()
#Clasificacion binaria, positivo o negativo
#entrenamiento
#Callback para guardar el mejor modelo de las mejores epocas.
checkpoint = ModelCheckpoint('deteccion_texto.h5',
monitor='val_binary_accuracy',
verbose=1,
save_best_only=True,
save_weights_only=False,
mode='auto')
history = modelo.fit(xentrenamiento,
yentrenamiento,
descriptionEmbeddings,
#input_length=MAX_DESC_SEQUENCE_LENGTH,
mask_zero=True)(descriptionBranchI)
descriptionBranch = SpatialDropout1D(rate=0.2)(
descriptionBranch) #Masks the same embedding element for all tokens
descriptionBranch = BatchNormalization()(descriptionBranch)
descriptionBranch = Dropout(0.2)(descriptionBranch)
descriptionBranch = LSTM(units=30)(descriptionBranch)
descriptionBranch = BatchNormalization()(descriptionBranch)
descriptionBranch = Dropout(0.2, name="description")(descriptionBranch)
descriptionBranchO = Dense(len(set(classes)),
activation='softmax')(descriptionBranch)
descriptionModel = Model(inputs=descriptionBranchI, outputs=descriptionBranchO)
descriptionModel.compile(loss='sparse_categorical_crossentropy',
optimizer='adam',
metrics=['accuracy'])
start = time.time()
descriptionHistory = descriptionModel.fit(trainDescription,
classes,
epochs=nb_epoch,
batch_size=batch_size,
verbose=verbosity,
validation_split=validation_split,
callbacks=callbacks)
print("descriptionBranch finished after " +
str(datetime.timedelta(seconds=round(time.time() - start))))
descriptionModel.save(modelPath + 'descriptionBranchNorm.h5')
#####################
#2a.) Link Model for Domain
model.summary()
# model = Sequential()
# model.add(xception)
# model.add(layers.Dense(1000, activation='relu'))
# model.add(layers.Dense(1000, activation='relu'))
# model.add(Dropout(0.5))
# model.add(layers.Dense(num_classes, activation='softmax'))
model.compile(
loss='categorical_crossentropy',
optimizer='adam',
metrics = ['accuracy'])
loops = 1
for i in range(loops):
print ('\n\nEPOCH SET {}'.format(i))
nb_epochs = 2
history = model.fit_generator(
train_generator,
steps_per_epoch = train_generator.samples // batch_size,
validation_data = validation_generator,
validation_steps = validation_generator.samples // batch_size,
epochs = nb_epochs)
name = 'Xception_places_200_FE'
val_dir,
target_size=(IM_WIDTH, IM_HEIGHT),
batch_size=batch_size,
class_mode='categorical')
inception_model = InceptionResNetV2(include_top=False)
x = GlobalAveragePooling2D(name='avg_pool')(inception_model.output)
x = Dense(nb_classes, activation='softmax', name='predictions')(x)
model = Model(inception_model.input, x)
# model = load_model('boxes.h5')
for layer in model.layers:
layer.trainable = False
model.layers[-1].trainable = True
model.layers[-2].trainable = True
checkpoint = ModelCheckpoint("boxes_trained_epoch_{epoch}.h5",
monitor='val_loss',
save_weights_only=False,
save_best_only=True)
model.compile(optimizer=Adam(lr=0.001),
loss='categorical_crossentropy',
metrics=['accuracy'])
model.fit_generator(train_generator,
validation_data=validation_generator,
epochs=nb_epoch,
callbacks=[checkpoint])
#conv2 = Conv1D(128, 3, activation='tanh')(max_1)
#max_2 = MaxPooling1D(3)(conv2)
question_out_1 = Flatten()(question_dmax_2)
#out_1 = LSTM(128)(max_1)
merged_vector = merge([relation_out_1, question_out_1], mode='concat') # good
dense_1 = Dense(128, activation='relu')(merged_vector)
dense_2 = Dense(128, activation='relu')(dense_1)
dense_3 = Dense(128, activation='relu')(dense_2)
predictions = Dense(1, activation='sigmoid')(dense_3)
#predictions = Dense(len(labels_index), activation='softmax')(merged_vector)
model = Model(input=[tweet_relation, tweet_ques], output=predictions)
model.compile(optimizer='rmsprop',
loss='binary_crossentropy',
metrics=['accuracy'])
model.fit([rela_train, ques_train],
label_train,
nb_epoch=10,
batch_size=20,
verbose=1,
shuffle=True)
json_string = model.to_json() # json_string = model.get_config()
open('my_model_architecture.json', 'w').write(json_string)
model.save_weights('my_model_weights.h5')
score = model.evaluate([rela_train, ques_train], label_train, verbose=0)
print('train score:', score[0])
print('train accuracy:', score[1])
z = layers.MaxPooling2D((3, 3))(z)
z = layers.Conv2D(128, (1, 1), padding='same')(z)
z = layers.ReLU()(z)
z = layers.Conv2D(64, (1, 1))(z)
z = layers.LeakyReLU(alpha=0.3)(z)
z = layers.Conv2D(32, (1, 1))(z)
z = layers.ReLU()(z)
z = layers.Flatten()(z)
# Von folgendem Layer werden die Gewichtungen erfasst
z = layers.Dense(32, kernel_regularizer=l1(0.001))(z)
z = layers.ReLU()(z)
model_output_1 = layers.Dense(1, activation='sigmoid')(z)
model = Model(input_1, model_output_1)
model.summary()
model.compile(loss=['binary_crossentropy'],
optimizer=optimizers.Nadam(lr=1e-2),
metrics=['acc'])
path = os.path.join(os.getcwd(), 'logs/')
callbacks_list = [keras.callbacks.ReduceLROnPlateau(monitor='val_loss',
factor=0.2,
patience=5)]
STEP_SIZE_TRAIN = train_generator.n // train_generator.batch_size
STEP_SIZE_VALID = validation_generator.n // validation_generator.batch_size
STEP_SIZE_TEST = test_generator.n // test_generator.batch_size
history = model.fit_generator(
generator=train_generator,
steps_per_epoch=STEP_SIZE_TRAIN,
epochs=40,
callbacks=callbacks_list,
validation_data=validation_generator,
class PolicyValueNetwork:
""" AlphaZero Residual-CNN """
def __init__(self, model_file=None):
# Build Network Architecture
input_shape = Board().encoded_states().shape # (6, 15, 15)
inputs = Input(input_shape)
shared_net = Sequential([
*ConvBlock(32, input_shape=input_shape),
*ConvBlock(64),
*ConvBlock(128)
], "shared_net")
policy_head = Sequential([
shared_net,
*ConvBlock(4, (1, 1), "relu"),
Flatten(),
Dense(Game["board_size"], kernel_regularizer=l2()),
Activation("softmax")
], "policy_head")
value_head = Sequential([
shared_net,
*ConvBlock(2, (1, 1), "relu"),
Flatten(),
Dense(64, activation="relu", kernel_regularizer=l2()),
Dense(1, kernel_regularizer=l2()),
Activation("tanh")
], "value_head")
self.model = Model(
inputs,
[value_head(inputs), policy_head(inputs)]
)
if model_file is not None:
self.restore_model(model_file)
def compile(self, opt):
"""
Optimization and Loss definition
"""
self.model.compile(
optimizer=sgd(),
loss=["mse", "categorical_crossentropy"]
)
def eval_state(self, state):
"""
Evaluate a board state.
"""
vp = self.model.predict_on_batch(state.encoded_states()[np.newaxis, :])
# format to (float, np.array((255,1),dtype=float)) structure
return vp[0][0][0], vp[1][0]
def train_step(self, optimizer):
"""
One Network Tranning step.
"""
opt = self.model.optimizer
K.set_value(opt.lr, optimizer["lr"])
K.set_value(opt.momentum, optimizer["momentum"])
# loss = self.model.train_on_batch(inputs, [winner, probs])
# return loss
def save_model(self, filename):
base_path = "{}/keras".format(TRAINING_CONFIG["model_path"])
if not os.path.exists(base_path):
os.mkdir(base_path)
self.model.save_weights("{}/{}.h5".format(base_path, filename))
def restore_model(self, filename):
base_path = "{}/keras".format(TRAINING_CONFIG["model_path"])
if os.path.exists("{}/{}.h5".format(base_path, filename)):
self.model.load_weights("{}/{}.h5".format(base_path, filename))
# Let's use the 'mixed7' layer as the input to our model
last_layer = pre_trained_model.get_layer('mixed7')
print("last later output shape: ", last_layer.output_shape)
last_output = last_layer.output # this is the input to our own model
# building our own model to on top of last_layer
x = layers.Flatten()(last_output) # flattening output layer to 1-dim
x = layers.Dense(units=1024, activation='relu')(x)
x = layers.Dropout(rate=0.2)(x)
x = layers.Dense(units=1, activation='sigmoid')(x)
model = Model(pre_trained_model.input, x)
model.compile(optimizer=RMSprop(lr=0.0001),
loss='binary_crossentropy',
metrics=['acc']
)
# now for the data
base_dir = 'utils/cats_and_dogs_filtered'
train_dir = os.path.join(base_dir, 'train')
validation_dir = os.path.join(base_dir, 'validation')
train_cats_dir = os.path.join(train_dir, 'cats')
train_dogs_dir = os.path.join(train_dir, 'dogs')
validation_cats_dir = os.path.join(validation_dir, 'cats')
validation_dogs_dir = os.path.join(validation_dir, 'dogs')
train_cats_filenames = os.listdir(train_cats_dir)
train_dogs_filenames = os.listdir(train_dogs_dir)
class CharacterTagger:
"""A class for character-based neural morphological tagger
Parameters:
symbols: character vocabulary
tags: morphological tags vocabulary
word_rnn: the type of character-level network (only `cnn` implemented)
char_embeddings_size: the size of character embeddings
char_conv_layers: the number of convolutional layers on character level
char_window_size: the width of convolutional filter (filters).
It can be a list if several parallel filters are applied, for example, [2, 3, 4, 5].
char_filters: the number of convolutional filters for each window width.
It can be a number, a list (when there are several windows of different width
on a single convolution layer), a list of lists, if there
are more than 1 convolution layers, or **None**.
If **None**, a layer with width **width** contains
min(**char_filter_multiple** * **width**, 200) filters.
char_filter_multiple: the ratio between filters number and window width
char_highway_layers: the number of highway layers on character level
conv_dropout: the ratio of dropout between convolutional layers
highway_dropout: the ratio of dropout between highway layers,
intermediate_dropout: the ratio of dropout between convolutional
and highway layers on character level
lstm_dropout: dropout ratio in word-level LSTM
word_vectorizers: list of parameters for additional word-level vectorizers,
for each vectorizer it stores a pair of vectorizer dimension and
the dimension of the corresponding word embedding
word_lstm_layers: the number of word-level LSTM layers
word_lstm_units: hidden dimensions of word-level LSTMs
word_dropout: the ratio of dropout before word level (it is applied to word embeddings)
regularizer: l2 regularization parameter
verbose: the level of verbosity
"""
def __init__(self,
symbols: DefaultVocabulary,
tags: DefaultVocabulary,
word_rnn: str = "cnn",
char_embeddings_size: int = 16,
char_conv_layers: int = 1,
char_window_size: Union[int, List[int]] = 5,
char_filters: Union[int, List[int]] = None,
char_filter_multiple: int = 25,
char_highway_layers: int = 1,
conv_dropout: float = 0.0,
highway_dropout: float = 0.0,
intermediate_dropout: float = 0.0,
lstm_dropout: float = 0.0,
word_vectorizers: List[Tuple[int, int]] = None,
word_lstm_layers: int = 1,
word_lstm_units: Union[int, List[int]] = 128,
word_dropout: float = 0.0,
regularizer: float = None,
verbose: int = 1):
self.symbols = symbols
self.tags = tags
self.word_rnn = word_rnn
self.char_embeddings_size = char_embeddings_size
self.char_conv_layers = char_conv_layers
self.char_window_size = char_window_size
self.char_filters = char_filters
self.char_filter_multiple = char_filter_multiple
self.char_highway_layers = char_highway_layers
self.conv_dropout = conv_dropout
self.highway_dropout = highway_dropout
self.intermediate_dropout = intermediate_dropout
self.lstm_dropout = lstm_dropout
self.word_dropout = word_dropout
self.word_vectorizers = word_vectorizers # a list of additional vectorizer dimensions
self.word_lstm_layers = word_lstm_layers
self.word_lstm_units = word_lstm_units
self.regularizer = regularizer
self.verbose = verbose
self._initialize()
self.build()
def _initialize(self):
if isinstance(self.char_window_size, int):
self.char_window_size = [self.char_window_size]
if self.char_filters is None or isinstance(self.char_filters, int):
self.char_filters = [self.char_filters] * len(self.char_window_size)
if len(self.char_window_size) != len(self.char_filters):
raise ValueError("There should be the same number of window sizes and filter sizes")
if isinstance(self.word_lstm_units, int):
self.word_lstm_units = [self.word_lstm_units] * self.word_lstm_layers
if len(self.word_lstm_units) != self.word_lstm_layers:
raise ValueError("There should be the same number of lstm layer units and lstm layers")
if self.word_vectorizers is None:
self.word_vectorizers = []
if self.regularizer is not None:
self.regularizer = kreg.l2(self.regularizer)
if self.verbose > 0:
log.info("{} symbols, {} tags in CharacterTagger".format(self.symbols_number_, self.tags_number_))
@property
def symbols_number_(self) -> int:
"""Character vocabulary size
"""
return len(self.symbols)
@property
def tags_number_(self) -> int:
"""Tag vocabulary size
"""
return len(self.tags)
def build(self):
"""Builds the network using Keras.
"""
word_inputs = kl.Input(shape=(None, MAX_WORD_LENGTH+2), dtype="int32")
inputs = [word_inputs]
word_outputs = self._build_word_cnn(word_inputs)
if len(self.word_vectorizers) > 0:
additional_word_inputs = [kl.Input(shape=(None, input_dim), dtype="float32")
for input_dim, dense_dim in self.word_vectorizers]
inputs.extend(additional_word_inputs)
additional_word_embeddings = [kl.Dense(dense_dim)(additional_word_inputs[i])
for i, (_, dense_dim) in enumerate(self.word_vectorizers)]
word_outputs = kl.Concatenate()([word_outputs] + additional_word_embeddings)
outputs, lstm_outputs = self._build_basic_network(word_outputs)
compile_args = {"optimizer": ko.nadam(lr=0.002, clipnorm=5.0),
"loss": "categorical_crossentropy", "metrics": ["accuracy"]}
self.model_ = Model(inputs, outputs)
self.model_.compile(**compile_args)
if self.verbose > 0:
self.model_.summary(print_fn=log.info)
return self
def _build_word_cnn(self, inputs):
"""Builds word-level network
"""
inputs = kl.Lambda(kb.one_hot, arguments={"num_classes": self.symbols_number_},
output_shape=lambda x: tuple(x) + (self.symbols_number_,))(inputs)
char_embeddings = kl.Dense(self.char_embeddings_size, use_bias=False)(inputs)
conv_outputs = []
self.char_output_dim_ = 0
for window_size, filters_number in zip(self.char_window_size, self.char_filters):
curr_output = char_embeddings
curr_filters_number = (min(self.char_filter_multiple * window_size, 200)
if filters_number is None else filters_number)
for _ in range(self.char_conv_layers - 1):
curr_output = kl.Conv2D(curr_filters_number, (1, window_size),
padding="same", activation="relu",
data_format="channels_last")(curr_output)
if self.conv_dropout > 0.0:
curr_output = kl.Dropout(self.conv_dropout)(curr_output)
curr_output = kl.Conv2D(curr_filters_number, (1, window_size),
padding="same", activation="relu",
data_format="channels_last")(curr_output)
conv_outputs.append(curr_output)
self.char_output_dim_ += curr_filters_number
if len(conv_outputs) > 1:
conv_output = kl.Concatenate(axis=-1)(conv_outputs)
else:
conv_output = conv_outputs[0]
highway_input = kl.Lambda(kb.max, arguments={"axis": -2})(conv_output)
if self.intermediate_dropout > 0.0:
highway_input = kl.Dropout(self.intermediate_dropout)(highway_input)
for i in range(self.char_highway_layers - 1):
highway_input = Highway(activation="relu")(highway_input)
if self.highway_dropout > 0.0:
highway_input = kl.Dropout(self.highway_dropout)(highway_input)
highway_output = Highway(activation="relu")(highway_input)
return highway_output
def _build_basic_network(self, word_outputs):
"""
Creates the basic network architecture,
transforming word embeddings to intermediate outputs
"""
if self.word_dropout > 0.0:
lstm_outputs = kl.Dropout(self.word_dropout)(word_outputs)
else:
lstm_outputs = word_outputs
for j in range(self.word_lstm_layers-1):
lstm_outputs = kl.Bidirectional(
kl.LSTM(self.word_lstm_units[j], return_sequences=True,
dropout=self.lstm_dropout))(lstm_outputs)
lstm_outputs = kl.Bidirectional(
kl.LSTM(self.word_lstm_units[-1], return_sequences=True,
dropout=self.lstm_dropout))(lstm_outputs)
pre_outputs = kl.TimeDistributed(
kl.Dense(self.tags_number_, activation="softmax",
activity_regularizer=self.regularizer),
name="p")(lstm_outputs)
return pre_outputs, lstm_outputs
def _transform_batch(self, data, labels=None, transform_to_one_hot=True):
data, additional_data = data[0], data[1:]
L = max(len(x) for x in data)
X = np.array([self._make_sent_vector(x, L) for x in data])
X = [X] + [np.array(x) for x in additional_data]
if labels is not None:
Y = np.array([self._make_tags_vector(y, L) for y in labels])
if transform_to_one_hot:
Y = to_one_hot(Y, len(self.tags))
return X, Y
else:
return X
def train_on_batch(self, data: List[Iterable], labels: Iterable[list]) -> None:
"""Trains model on a single batch
Args:
data: a batch of word sequences
labels: a batch of correct tag sequences
Returns:
the trained model
"""
X, Y = self._transform_batch(data, labels)
self.model_.train_on_batch(X, Y)
def predict_on_batch(self, data: Union[list, tuple],
return_indexes: bool = False) -> List[List[str]]:
"""
Makes predictions on a single batch
Args:
data: a batch of word sequences together with additional inputs
return_indexes: whether to return tag indexes in vocabulary or tags themselves
Returns:
a batch of label sequences
"""
X = self._transform_batch(data)
objects_number, lengths = len(X[0]), [len(elem) for elem in data[0]]
Y = self.model_.predict_on_batch(X)
labels = np.argmax(Y, axis=-1)
answer: List[List[str]] = [None] * objects_number
for i, (elem, length) in enumerate(zip(labels, lengths)):
elem = elem[:length]
answer[i] = elem if return_indexes else self.tags.idxs2toks(elem)
return answer
def _make_sent_vector(self, sent: List, bucket_length: int =None) -> np.ndarray:
"""Transforms a sentence to Numpy array, which will be the network input.
Args:
sent: input sentence
bucket_length: the width of the bucket
Returns:
A 3d array, answer[i][j][k] contains the index of k-th letter
in j-th word of i-th input sentence.
"""
bucket_length = bucket_length or len(sent)
answer = np.zeros(shape=(bucket_length, MAX_WORD_LENGTH+2), dtype=np.int32)
for i, word in enumerate(sent):
answer[i, 0] = self.tags.tok2idx("BEGIN")
m = min(len(word), MAX_WORD_LENGTH)
for j, x in enumerate(word[-m:]):
answer[i, j+1] = self.symbols.tok2idx(x)
answer[i, m+1] = self.tags.tok2idx("END")
answer[i, m+2:] = self.tags.tok2idx("PAD")
return answer
def _make_tags_vector(self, tags, bucket_length=None) -> np.ndarray:
"""Transforms a sentence of tags to Numpy array, which will be the network target.
Args:
tags: input sentence of tags
bucket_length: the width of the bucket
Returns:
A 2d array, answer[i][j] contains the index of j-th tag in i-th input sentence.
"""
bucket_length = bucket_length or len(tags)
answer = np.zeros(shape=(bucket_length,), dtype=np.int32)
for i, tag in enumerate(tags):
answer[i] = self.tags.tok2idx(tag)
return answer
def save(self, outfile) -> None:
"""Saves model weights to a file
Args:
outfile: file with model weights (other model components should be given in config)
"""
self.model_.save_weights(outfile)
def load(self, infile) -> None:
"""Loads model weights from a file
Args:
infile: file to load model weights from
"""
self.model_.load_weights(infile)
max_pool = MaxPooling2D(pool_size=(2, 2), strides=(2, 2),
padding='same')(multi)
incept_1 = inception_module(max_pool, 72, 64)
max_pool = MaxPooling2D(pool_size=(2, 2), strides=(2, 2),
padding='same')(incept_1)
incept_2 = inception_module(max_pool, 128, 96)
max_pool = MaxPooling2D(pool_size=(2, 2), strides=(2, 2),
padding='same')(incept_2)
output = Flatten()(max_pool)
output = Dense(512, activation='relu')(output)
output = Dense(20, activation='softmax')(output)
model = Model(inputs=input_spectrum, outputs=output)
print(model.summary())
adam = Adam()
model.compile(adam, loss='categorical_crossentropy', metrics=['accuracy'])
history = model.fit(train_x,
train_y,
epochs=1,
batch_size=256,
validation_data=(val_x, val_y))
plot_accuracy(history)
plot_confusion_matrix(model, val_x, labeled_val_y)
pred_test_y = np.argmax(model.predict(test_x), axis=1)
np.save('results.npy', pred_test_y)
def build(self,
sentence_length,
word_length,
num_labels,
num_intent_labels,
word_vocab_size,
char_vocab_size,
word_emb_dims=100,
char_emb_dims=25,
char_lstm_dims=25,
tagger_lstm_dims=100,
dropout=0.2,
embedding_matrix=None):
"""
Build a model
Args:
sentence_length (int): max sentence length
word_length (int): max word length (in characters)
num_labels (int): number of slot labels
num_intent_labels (int): number of intent classes
word_vocab_size (int): word vocabulary size
char_vocab_size (int): character vocabulary size
word_emb_dims (int, optional): word embedding dimensions
char_emb_dims (int, optional): character embedding dimensions
char_lstm_dims (int, optional): character feature LSTM hidden size
tagger_lstm_dims (int, optional): tagger LSTM hidden size
dropout (float, optional): dropout rate
embedding_matrix (dict, optional): external word embedding dictionary
"""
if embedding_matrix is not None:
# load pre-trained word embeddings into an Embedding layer
# note that we set trainable = False so as to keep the embeddings fixed
embedding_layer = Embedding(word_vocab_size,
word_emb_dims,
weights=[embedding_matrix],
input_length=sentence_length,
trainable=True,
name='word_embedding_layer')
else:
# learn embeddings ourselves
embedding_layer = Embedding(word_vocab_size, word_emb_dims,
input_length=sentence_length,
name='word_embedding_layer')
# create word embedding input and embedding layer
words_input = Input(shape=(sentence_length,), name='words_input')
word_embeddings = embedding_layer(words_input)
word_embeddings = Dropout(dropout)(word_embeddings)
# create word character input and embeddings layer
word_chars_input = Input(shape=(sentence_length, word_length), name='word_chars_input')
char_embedding_layer = Embedding(char_vocab_size, char_emb_dims,
input_length=word_length, name='char_embedding_layer')
# apply embedding to each word
char_embeddings = TimeDistributed(char_embedding_layer)(word_chars_input)
# feed dense char vectors into BiLSTM
char_embeddings = TimeDistributed(Bidirectional(LSTM(char_lstm_dims)))(char_embeddings)
char_embeddings = Dropout(dropout)(char_embeddings)
# first BiLSTM layer (used for intent classification)
first_bilstm_layer = Bidirectional(
LSTM(tagger_lstm_dims, return_sequences=True, return_state=True))
first_lstm_out = first_bilstm_layer(word_embeddings)
lstm_y_sequence = first_lstm_out[:1][0] # save y states of the LSTM layer
states = first_lstm_out[1:]
hf, cf, hb, cb = states # extract last hidden states
h_state = concatenate([hf, hb], axis=-1)
intent_out = Dense(num_intent_labels, activation='softmax',
name='intent_classifier_output')(h_state)
# create the 2nd feature vectors
combined_features = concatenate([lstm_y_sequence, char_embeddings], axis=-1)
# 2nd BiLSTM layer for label classification
second_bilstm_layer = Bidirectional(
LSTM(tagger_lstm_dims, return_sequences=True))(combined_features)
second_bilstm_layer = Dropout(dropout)(second_bilstm_layer)
# feed BiLSTM vectors into CRF
crf = CRF(num_labels, sparse_target=False)
labels_out = crf(second_bilstm_layer)
# compile the model
model = Model(inputs=[words_input, word_chars_input],
outputs=[intent_out, labels_out])
# define losses and metrics
loss_f = {'intent_classifier_output': 'categorical_crossentropy',
'crf_1': crf.loss_function}
metrics = {'intent_classifier_output': 'categorical_accuracy',
'crf_1': crf.accuracy}
model.compile(loss=loss_f,
optimizer='adam',
metrics=metrics)
self.model = model
class NNClassifier(ClassifierMixin):
"""
Neural Network classifier, implements the same methods as the sklearn models to make it simple to add
"""
# noinspection PyTypeChecker
def __init__(self, **kwargs: Dict[str, Union[int, str, float]]):
"""initializes the Neural Network classifier
:param kwargs: configuration containing the predictive_model parameters, encoding and training parameters
"""
self._n_hidden_layers = int(kwargs['n_hidden_layers'])
self._n_hidden_units = int(kwargs['n_hidden_units'])
self._activation = str(kwargs['activation'])
self._n_epochs = int(kwargs['n_epochs'])
self._encoding = str(kwargs['encoding'])
self._dropout_rate = float(kwargs['dropout_rate'])
self._is_binary_classifier = bool(kwargs['is_binary_classifier'])
self._encoding_parser = EncodingParser(self._encoding, self._is_binary_classifier,
task=PredictiveModels.CLASSIFICATION.value)
self._model = None
def fit(self, train_data: DataFrame, targets: ndarray) -> None:
"""creates and fits the predictive_model
first the encoded data is parsed, then the predictive_model created and then trained
:param train_data: encoded training dataset
:param targets: encoded target dataset
"""
targets = DataFrame(targets, columns=['label'])
train_data = self._encoding_parser.parse_training_dataset(train_data)
targets = self._encoding_parser.parse_targets(targets)
model_inputs = Input(train_data.shape[1:])
predicted = model_inputs
if self._encoding in ['simpleIndex', 'complex', 'lastPayload']:
predicted = Flatten()(predicted)
for _ in range(self._n_hidden_layers):
predicted = Dense(self._n_hidden_units, activation=self._activation)(predicted)
predicted = Dropout(self._dropout_rate)(predicted)
if self._is_binary_classifier:
predicted = Dense(1, activation='sigmoid')(predicted)
else:
predicted = Dense(targets.shape[1], activation='softmax')(predicted)
self._model = Model(model_inputs, predicted)
if self._is_binary_classifier:
self._model.compile(loss='binary_crossentropy', optimizer='adam')
else:
self._model.compile(loss='categorical_crossentropy', optimizer='adam')
self._model.fit(train_data, targets, epochs=self._n_epochs)
def predict(self, test_data: DataFrame) -> ndarray:
"""returns predictive_model predictions
parses the encoded test dataset, then returns the predictive_model predictions
:param test_data: encoded test dataset
:return: predictive_model predictions
"""
test_data = self._encoding_parser.parse_testing_dataset(test_data)
predictions = self._model.predict(test_data)
if self._is_binary_classifier:
predictions = predictions.astype(bool)
else:
predictions = np.argmax(predictions, -1)
return predictions
def predict_proba(self, test_data: DataFrame) -> ndarray:
"""returns the classification probability
parses the test dataset and returns the raw prediction probabilities of the predictive_model
:param test_data: encoded test dataset
:return: predictive_model prediction probabilities
"""
test_data = self._encoding_parser.parse_testing_dataset(test_data)
predictions = self._model.predict(test_data)
if self._is_binary_classifier:
predictions = np.max(predictions, -1)
predictions = np.vstack((1 - predictions, predictions)).T
return predictions
def reset(self) -> None:
"""
class EncDecIntentModel(IntentExtractionModel):
"""
Encoder Decoder Deep LSTM Tagger Model
"""
def __init__(self):
super(EncDecIntentModel, self).__init__()
def build(self,
sentence_length,
vocab_size,
tag_labels,
token_emb_size=100,
encoder_depth=1,
decoder_depth=1,
lstm_hidden_size=100,
encoder_dropout=0.5,
decoder_dropout=0.5,
emb_model_path=None):
"""
Build the model
Args:
sentence_length (int): max sentence length
vocab_size (int): vocabulary size
tag_labels (int): number of tag labels
token_emb_size (int, optional): token embedding vector size
encoder_depth (int, optional): number of encoder LSTM layers
decoder_depth (int, optional): number of decoder LSTM layers
lstm_hidden_size (int, optional): LSTM layers hidden size
encoder_dropout (float, optional): encoder dropout
decoder_dropout (float, optional): decoder dropout
emb_model_path (str, optional): external embedding model path
"""
tokens_input, token_emb = self._create_input_embed(sentence_length,
emb_model_path is not None,
token_emb_size,
vocab_size)
benc_in = token_emb
assert encoder_depth > 0, 'Encoder depth must be > 0'
for i in range(encoder_depth):
bencoder = LSTM(lstm_hidden_size, return_sequences=True, return_state=True,
go_backwards=True, dropout=encoder_dropout,
name='encoder_blstm_{}'.format(i))(benc_in)
benc_in = bencoder[0]
b_states = bencoder[1:]
benc_h, bene_c = b_states
decoder_inputs = token_emb
assert decoder_depth > 0, 'Decoder depth must be > 0'
for i in range(decoder_depth):
decoder = LSTM(lstm_hidden_size, return_sequences=True,
name='decoder_lstm_{}'.format(i))(decoder_inputs,
initial_state=[benc_h,
bene_c])
decoder_inputs = decoder
decoder_outputs = Dropout(decoder_dropout)(decoder)
decoder_predictions = TimeDistributed(
Dense(tag_labels, activation='softmax'),
name='decoder_classifier')(decoder_outputs)
self.model = Model(tokens_input, decoder_predictions)
self.model.compile(optimizer='rmsprop', loss='categorical_crossentropy',
metrics=['categorical_accuracy'])
# %% from keras import Input, layers from keras import Model input_tensor = Input(shape=(64,)) x = layers.Dense(32, activation='relu')(input_tensor) x = layers.Dense(32, activation='relu')(x) output_tensor = layers.Dense(10, activation='softmax')(x) model = Model(input_tensor, output_tensor) model.summary() # %% import numpy as np model.compile(optimizer='rmsprop', loss='categorical_crossentropy') x_train = np.random.random((1000, 64)) y_train = np.random.random((1000, 10)) model.fit(x_train, y_train, epochs=10, batch_size=128) score = model.evaluate(x_train, y_train)