def buildModel(embeddingMatrix):
"""Constructs the architecture of the modelEMOTICONS_TOKEN[list_str[index]]
Input:
embeddingMatrix : The embedding matrix to be loaded in the embedding layer.
Output:
model : A basic LSTM model
"""
sequence = Input(shape=(MAX_SEQUENCE_LENGTH, ), dtype='float32')
embeddingLayer = Embedding(embeddingMatrix.shape[0],
EMBEDDING_DIM,
weights=[embeddingMatrix],
input_length=MAX_SEQUENCE_LENGTH,
trainable=False)(sequence)
embedded = Highway()(embeddingLayer)
# embedded = Dropout(0.25)(embedded)
embedded = Bidirectional(
LSTM(LSTM_DIM, dropout=DROPOUT, return_sequences=True))(embedded)
enc = Bidirectional(LSTM(LSTM_DIM, dropout=DROPOUT))(embedded)
fc1 = Dense(128, activation="relu")(enc)
fc2_dropout = Dropout(0.25)(fc1)
output = Dense(NUM_CLASSES, activation='sigmoid')(fc2_dropout)
rmsprop = optimizers.rmsprop(lr=LEARNING_RATE)
model = Model(inputs=sequence, outputs=output)
model.compile(loss='categorical_crossentropy',
optimizer=rmsprop,
metrics=['acc'])
return model
def naics_hw_noproj_encoder(input_layer, n_layers=2):
layers = [input_layer]
for n in range(n_layers):
previous_layer = layers[n]
layer = Highway(activation='relu',
name='naics_hw%s' % n)(previous_layer)
layers.append(layer)
return layers[-1]
def _enhance_D(self, c):
# adding another enhancing layer for D
if self.enhanceD == "LSTM":
l = int(c.get_shape()[-1])
c = Reshape((1,l))(c)
c = LSTM(l)(c)
elif self.enhanceD == "HW":
c = Highway()(c)
return c
def blink_net(shape=(64, 64), nb_channels=1):
logger.info('generating net with input shape ({})'.format(', '.join(
str(s) for s in shape)))
img_width, img_height = shape
eye = Input(shape=(nb_channels, img_width, img_height))
eye_model = Sequential()
eye_model.add(
Convolution2D(64,
3,
3,
border_mode='valid',
activation='relu',
input_shape=(nb_channels, img_width, img_height)))
eye_model.add(Dropout(0.25))
eye_model.add(
Convolution2D(32, 3, 3, border_mode='valid', activation='relu'))
eye_model.add(Dropout(0.25))
eye_model.add(Flatten(input_shape=(nb_channels, img_width, img_height)))
eye_model.add(Dense(1024, activation='relu'))
eye_model.add(Dropout(0.25))
eye_model.add(Highway(activation='relu'))
eye_model.add(Dropout(0.25))
eye_model.add(Highway(activation='relu'))
eye_model.add(Dropout(0.25))
eye_model.add(Highway(activation='relu'))
eye_model.add(Dense(512, activation='relu'))
eye_model.add(Dropout(0.2))
eye_model.add(Dense(128, activation='relu'))
eye_model.add(Dropout(0.2))
eye_model.add(Dense(2, activation='softmax', name='pose'))
logger.info('compiling with Adam and mse')
eye_model.compile('adam', 'categorical_crossentropy', metrics=['acc'])
return eye_model
def lstm():
model = kr.Sequential()
model.add(BatchNormalization(input_shape=(1, 465)))
model.add(
LSTM(64,
return_sequences=True,
kernel_initializer='he_normal',
use_bias=True,
bias_initializer=kr.initializers.one(),
unit_forget_bias=True,
kernel_regularizer=kr.regularizers.l1_l2(0.001, 0.0001)))
model.add(LeakyReLU())
model.add(
LSTM(64,
return_sequences=False,
go_backwards=True,
kernel_initializer='he_normal'))
model.add(Highway())
model.add(GaussianDropout(0.5))
model.add(LeakyReLU())
model.add(BatchNormalization())
model.add(Dense(32))
model.add(LeakyReLU())
model.add(BatchNormalization())
model.add(Highway())
model.add(Dense(64))
model.add(LeakyReLU())
model.add(BatchNormalization())
model.add(Highway())
model.add(Dense(128))
model.add(LeakyReLU())
model.add(BatchNormalization())
model.add(Highway())
model.add(Dense(256))
model.add(LeakyReLU())
model.add(BatchNormalization())
model.add(Dense(1))
sgd = kr.optimizers.sgd(lr=0.1,
momentum=0.1,
decay=0.001,
nesterov=True,
clipnorm=3)
model.compile(loss='mape', optimizer=sgd, metrics=['mae', 'mse'])
return model
def build_network(concept_dic, embeddings_file, EMBEDDING_DIM=100, MAX_SENSE_LENGTH = 5, CONTEXT_WINDOW_SIZE = 5,
PRE_TRAINED=True, UPDATABLE=True,
dropout_rate=0.3,
hidden_activation="relu", highway_activation="sigmoid", output_activation="linear",
optimizer="adam", print_model=False):
INPUTS = []
LEFT_RIGHT_CENTER = []
embedding_layer = create_embedding(concept_dic, embeddings_file,
EMBEDDING_DIM, MAX_SENSE_LENGTH, PRE_TRAINED, UPDATABLE)
for i in range(2 * CONTEXT_WINDOW_SIZE + 1):
"""Creating network's pipes one-by-one (from left to right)"""
context_term_input = Input(shape=(MAX_SENSE_LENGTH,), dtype='int32')
INPUTS.append(context_term_input)
context_term_embedding = embedding_layer(context_term_input)
pipe = MaxPooling1D(pool_size=MAX_SENSE_LENGTH)(context_term_embedding)
pipe = Flatten()(pipe)
LEFT_RIGHT_CENTER.append(pipe)
left = Merge(mode='max')(LEFT_RIGHT_CENTER[0:CONTEXT_WINDOW_SIZE])
left_dense = Dense(units=EMBEDDING_DIM, activation=hidden_activation)(left)
left_dense_dropout = Dropout(dropout_rate)(left_dense)
right = Merge(mode='max')(LEFT_RIGHT_CENTER[CONTEXT_WINDOW_SIZE:CONTEXT_WINDOW_SIZE * 2])
right_dense = Dense(units=EMBEDDING_DIM, activation=hidden_activation)(right)
right_dense_dropout = Dropout(dropout_rate)(right_dense)
context = Merge(mode='max')([left_dense_dropout, right_dense_dropout])
centre = LEFT_RIGHT_CENTER[-1]
#centre_dense = Dense(units=EMBEDDING_DIM, activation=hidden_activation)(centre)
#centre__dense_dropout = Dense(units=EMBEDDING_DIM, activation=hidden_activation)(centre_dense)
merge_instance = Concatenate(axis=-1)([context, centre])
merge_instance = Highway(activation=highway_activation)(merge_instance)
# merge_instance = Dense(units=EMBEDDING_DIM * 2, activation=hidden_activation)(merge_instance)
# merge_instance = Dropout(dropout_rate)(merge_instance)
merge_instance = Dense(units=EMBEDDING_DIM, activation=hidden_activation)(merge_instance)
merge_instance = Dropout(dropout_rate)(merge_instance)
prediction = Dense(units=1, activation=output_activation)(merge_instance)
model = Model(inputs=INPUTS, outputs=prediction)
model.compile(loss='mean_squared_error', optimizer=optimizer)
if print_model:
print(model.summary())
return model, embedding_layer
def _add_skip_layers(self, tensor_list, layer_count, activation="relu", regularization=None):
for layer_index in range(layer_count):
new_list = []
for i, item in enumerate(tensor_list):
item = TimeDistributed(
Highway(activation=activation, W_regularizer=regularization, b_regularizer=regularization))(item)
new_list.append(item)
tensor_list = new_list
return tensor_list
def default_model(self):
product_ecfp4 = Input(shape=(16384, ))
reaction_ecfp4 = Input(shape=(2048, ))
product = Dense(activation='elu', units=1024)(product_ecfp4)
reaction = Dense(activation='elu', units=1024)(reaction_ecfp4)
product = Dropout(0.3)(product)
product = Highway(activation='elu')(product)
product = Highway(activation='elu')(product)
product = Highway(activation='elu')(product)
product = Highway(activation='elu')(product)
product = Highway(activation='elu')(product)
cosine_similarities = Dot(normalize=True, axes=-1)([product, reaction])
Y = Activation('sigmoid')(cosine_similarities)
model = Model(input=[product_ecfp4, reaction_ecfp4], output=Y)
return model
def get_fc_model():
width = 40
depth = 6
model = Sequential()
model.add(Dense(width, input_dim=3))
model.add(PReLU())
for d in range(depth):
model.add(Highway())
model.add(PReLU())
model.add(Dense(3))
model.compile(loss='mae', optimizer='rmsprop')
return model
def default_model(self):
product_ecfp4 = Input(shape=(self.n_feats, ))
product = Dense(512, activation='elu')(product_ecfp4)
product = Dropout(0.3)(product)
product = Highway(activation='elu')(product)
product = Dropout(rate=0.1)(product)
product = Highway(activation='elu')(product)
product = Dropout(rate=0.1)(product)
product = Highway(activation='elu')(product)
product = Dropout(rate=0.1)(product)
product = Highway(activation='elu')(product)
product = Dropout(rate=0.1)(product)
product = Highway(activation='elu')(product)
product = Dropout(rate=0.1)(product)
product = Dense(self.n_classes, activation="relu")(product)
Y = Activation('softmax')(product)
model = Model(input=product_ecfp4, output=Y)
return model
def build_model(config):
config = get_data(config)
base_name = 'out'
if config['hedge'] == True:
outs = [''] * config['n_layers']
out_name = [''] * config['n_layers']
N = config['n_layers']
for i in range(len(outs)):
outs[i] = base_name + str(i)
out_name[i] = base_name + str(i)
else:
outs = base_name
out_name = [base_name]
N = config['n_layers'] - 1
in_name = 'in0'
inputs = Input(config['input_size'], name=in_name)
for j in range(N):
if j == 0:
layer = Dense(config['hidden_num'])(inputs)
layer = Activation(config['activation'])(layer)
if config['hedge'] == True:
outs[j] = Dense(config['output_size'],
activation='softmax',
name=outs[j])(layer)
continue
if config['Highway'] == False:
layer = Dense(config['hidden_num'])(layer)
layer = Activation(config['activation'])(layer)
else:
layer = Highway(activation=config['activation'])(layer)
if config['hedge'] == True:
outs[j] = Dense(config['output_size'],
activation='softmax',
name=outs[j])(layer)
if config['hedge'] == False:
outs = Dense(config['output_size'], activation='softmax',
name=outs)(layer)
model = Model(input=inputs, output=outs)
return (model, in_name, out_name)
def add_layer(model, width, activation="tanh"):
model.add(Highway(bias=True))
#model.add(Dense(bias=True, output_dim=width))
model.add(Activation(activation))
return model
def naics_hw3_encoder(input_layer, output_dim):
h1 = Highway(activation='relu')(input_layer)
h2 = Highway(activation='relu')(h1)
h3 = Highway(activation='relu')(h2)
return Dense(output_dim, name='naics_linear_proj')(h3)
def PPI_model_builder(EMBEDDING_DIM,
model_ind,
MAX_SEQUENCE_LENGTH,
WORD_EMBEDDINGS,
SUB_ONTOLOGY_work,
word_indeces,
ACTIVATION_HIDDEN,
ACTIVATION_HIGHWAY,
ACTIVATION_OUTPUT,
DROPOUT,
OPTIMIZER,
TRANSFER_LEARNING=False,
PRE_TRAINED=True,
UPDATABLE=True,
PRINT_deepSimDEF_SUMMARY=False):
EMBEDDINGS = {}
INPUTS = []
DENSES = []
CHANNELS = []
CHANNELS2 = []
Dense1_weights = []
if TRANSFER_LEARNING:
# load json and create model
# json_file = open('model_repository/model_PPI_' + str(ind) + '.json', 'r')
json_file = open('model_repository/model_0.json', 'r')
loaded_model_json = json_file.read()
json_file.close()
loaded_model = model_from_json(loaded_model_json)
# load weights into new model
# loaded_model.load_weights('model_repository/model_PPI_' + str(ind) + '.h5')
loaded_model.load_weights('model_repository/model_0.h5')
# Dense1_weights = loaded_model.get_layer('gene_product_dense').get_weights()
print("Loaded model from disk")
model = loaded_model
model.compile(loss='binary_crossentropy', optimizer=OPTIMIZER, metrics=[fmeasure])
return model, 0
for i in range(2):
for sbo in SUB_ONTOLOGY_work:
protein_input = Input(shape=(MAX_SEQUENCE_LENGTH[sbo],), dtype='int32')
INPUTS.append(protein_input)
if sbo in EMBEDDINGS:
embedding_layer = EMBEDDINGS[sbo]
else:
if PRE_TRAINED:
# with using pre-trained word embedings
file_reader = open(WORD_EMBEDDINGS[sbo])
word_embeddings = {}
for line in file_reader:
values = line.split()
word = values[0]
vector = np.asarray(values[1:], dtype='float32')
word_embeddings[word] = vector
file_reader.close()
print 'Loaded', len(word_embeddings), 'word vectors for', sbo, '(Model ' + str(model_ind + 1) + ')'
embedding_size = len(word_embeddings[np.random.choice(word_embeddings.keys())])
embedding_matrix = np.zeros((len(word_indeces[sbo]) + 1, embedding_size)) - 300.0
for word, i in word_indeces[sbo].items():
embedding_vector = word_embeddings.get(word)
if embedding_vector is not None:
# words not found in embedding index will be all-zeros.
embedding_matrix[i] = embedding_vector
embedding_layer = Embedding(input_dim=len(word_indeces[sbo]) + 1,
output_dim=embedding_size,
weights=[embedding_matrix],
input_length=MAX_SEQUENCE_LENGTH[sbo],
trainable=UPDATABLE)
else:
# without using pre-trained word embedings
embedding_layer = Embedding(input_dim=len(word_indeces[sbo]) + 1,
output_dim=EMBEDDING_DIM,
input_length=MAX_SEQUENCE_LENGTH[sbo])
EMBEDDINGS[sbo] = embedding_layer
# protein_input = Input(shape=(MAX_SEQUENCE_LENGTH[sbo],), dtype='int32')
# INPUTS.append(protein_input)
GO_term = embedding_layer(protein_input)
Ch = MaxPooling1D(pool_size=MAX_SEQUENCE_LENGTH[sbo])(GO_term)
Ch = Flatten()(Ch)
CHANNELS.append(Ch)
num_pair = 2
for i in range(num_pair):
# for j in range(len(CHANNELS)/2):
if len(SUB_ONTOLOGY_work) > 1:
Mrg = Concatenate(axis=-1)(CHANNELS[i * len(SUB_ONTOLOGY_work):len(SUB_ONTOLOGY_work) * (i + 1)])
else:
Mrg = CHANNELS[i]
if len(DENSES) == 1:
Dns = DENSES[0]
else:
Dns = Dense(units=EMBEDDING_DIM * len(SUB_ONTOLOGY_work), activation=ACTIVATION_HIDDEN)
# Dns = Dense(units = EMBEDDING_DIM * len(SUB_ONTOLOGY_work), activation = ACTIVATION_HIDDEN, name='gene_product_dense',weights=Dense1_weights, trainable=UPDATABLE)
DENSES.append(Dns)
Ch = Dns(Mrg)
DrpOut = Dropout(DROPOUT)
Ch = DrpOut(Ch)
CHANNELS2.append(Ch)
merge = Concatenate(axis=-1)(CHANNELS2)
merge = Highway(activation=ACTIVATION_HIGHWAY, name="highway_layer")(merge)
merge = Dropout(DROPOUT)(merge)
merge = Dense(units=EMBEDDING_DIM * len(SUB_ONTOLOGY_work),
activation=ACTIVATION_HIDDEN)(merge)
merge = Dropout(DROPOUT)(merge)
preds = Dense(units=1, activation=ACTIVATION_OUTPUT)(merge)
model = Model(inputs=INPUTS, outputs=preds)
model.compile(loss='binary_crossentropy', optimizer=OPTIMIZER, metrics=[fmeasure])
if PRINT_deepSimDEF_SUMMARY:
print model.summary()
print "Model for Fold Number", model_ind + 1, "Instantiated!!\n"
return model, EMBEDDINGS
def naics_lp_hw_encoder(input_layer, output_dim):
l1 = Dense(output_dim, name='naics_linear_proj')(input_layer)
h1 = Highway(activation='relu')(l1)
return h1
def naics_hw_only_encoder(input_layer):
return Highway(activation='relu')(input_layer)
def deep_neural_net_gru(train_data_1, train_data_2, train_labels, test_data_1,
test_data_2, test_labels, max_len, len_chars,
bidirectional, hidden_units, selfattention, maxpooling,
alignment, shortcut, multiplerlu, onlyconcat, n):
early_stop = EarlyStopping(monitor='loss', patience=0, verbose=1)
checkpointer = ModelCheckpoint(
filepath="/home/amarinho/data-amarinho/checkpoint" + str(n) + ".hdf5",
verbose=1,
save_best_only=True)
gru1 = GRU(hidden_units, consume_less='gpu', return_sequences=True)
gru2 = GRU(hidden_units,
consume_less='gpu',
return_sequences=(alignment or selfattention or maxpooling))
if bidirectional:
gru1 = Bidirectional(gru1)
gru2 = Bidirectional(gru2)
# definition for left branch of the network
left_branch = Sequential()
left_branch.add(Masking(mask_value=0, input_shape=(max_len, len_chars)))
if shortcut:
left_branch_aux1 = Sequential()
left_branch_aux1.add(left_branch)
left_branch_aux1.add(gru1)
left_branch_aux2 = Sequential()
left_branch_aux2.add(
Merge([left_branch, left_branch_aux1], mode='concat'))
left_branch = left_branch_aux2
else:
left_branch.add(gru1)
left_branch.add(Dropout(0.01))
left_branch.add(gru2)
left_branch.add(Dropout(0.01))
# definition for right branch of the network
right_branch = Sequential()
right_branch.add(Masking(mask_value=0, input_shape=(max_len, len_chars)))
if shortcut:
right_branch_aux1 = Sequential()
right_branch_aux1.add(right_branch)
right_branch_aux1.add(gru1)
right_branch_aux2 = Sequential()
right_branch_aux2.add(
Merge([right_branch, right_branch_aux1], mode='concat'))
right_branch = right_branch_aux2
else:
right_branch.add(gru1)
right_branch.add(Dropout(0.01))
right_branch.add(gru2)
right_branch.add(Dropout(0.01))
# mechanisms used for building representations from the GRU states (e.g., through attention)
if alignment:
left_branch, right_branch = AlignmentAttention(left_branch,
right_branch)
if selfattention:
att = SelfAttLayer()
left_branch.add(att)
right_branch.add(att)
elif maxpooling:
left_branch.add(GlobalMaxPooling1DMasked())
right_branch.add(GlobalMaxPooling1DMasked())
elif alignment:
gru3 = GRU(hidden_units, consume_less='gpu', return_sequences=False)
if bidirectional: gru3 = Bidirectional(gru3)
left_branch.add(gru3)
right_branch.add(gru3)
# combine the two representations and produce the final classification
con_layer = Sequential(name="con_layer")
con_layer.add(
Merge([left_branch, right_branch], mode='concat', name="merge_con"))
mul_layer = Sequential(name="mul_layer")
mul_layer.add(
Merge([left_branch, right_branch], mode='mul', name="merge_mul"))
dif_layer = Sequential(name="dif_layer")
dif_layer.add(
Merge([left_branch, right_branch],
mode=lambda x: x[0] - x[1],
output_shape=lambda x: x[0],
name="merge_dif"))
final_model = Sequential(name="final_model")
if onlyconcat: final_model.add(con_layer)
else:
final_model.add(
Merge([con_layer, mul_layer, dif_layer],
mode='concat',
name="merge_threeconcat"))
final_model.add(Dropout(0.01))
final_model.add(Dense(hidden_units, activation='relu'))
final_model.add(Dropout(0.01))
if multiplerlu:
final_model.add(Highway(activation='relu'))
final_model.add(Dropout(0.01))
final_model.add(Highway(activation='relu'))
final_model.add(Dropout(0.01))
final_model.add(Dense(1, activation='sigmoid'))
print('Compiling...')
final_model.compile(optimizer='adam',
loss='binary_crossentropy',
metrics=['accuracy'])
print('Fitting...')
final_model.fit([train_data_1, train_data_2],
train_labels,
verbose=0,
validation_data=([test_data_1, test_data_2], test_labels),
callbacks=[early_stop, checkpointer],
nb_epoch=20)
start_time = time.time()
print("Evaluating ...")
aux = final_model.predict_classes([test_data_1, test_data_2]).ravel()
return aux, (time.time() - start_time)
def main(MODEL_FILE):
print "Loading hdf5's..."
test_dict = io.load('./data/test_dict_IPConv_ntuple_'+ RUN_NAME +'.h5')
train_dict = io.load('./data/train_dict_IPConv_ntuple_'+ RUN_NAME +'.h5')
X_train = train_dict['X']
y_train = train_dict['y']
X_test = test_dict['X']
y_test = test_dict['y']
n_features = X_test.shape[2]
# this is a df
ip3d = test_dict['ip3d']
print 'Building model...'
if (MODEL_FILE == 'CRNN'):
graph = build_graph(n_features)
model = Sequential()
model.add(graph)
# remove Maxout for tensorflow
model.add(MaxoutDense(64, 5, input_shape=graph.nodes['dropout'].output_shape[1:]))
model.add(Dense(64))
elif (MODEL_FILE == 'RNN'):
model = Sequential()
model.add(Masking(mask_value=-999, input_shape=(N_TRACKS, n_features)))
model.add(GRU(25))#, input_shape=(N_TRACKS, n_features))) #GRU
model.add(Dropout(0.2)) #0.2
# remove Maxout for tensorflow
model.add(MaxoutDense(64, 5)) #, input_shape=graph.nodes['dropout'].output_shape[1:]))
model.add(Dense(64))
model.add(Dropout(0.4))
model.add(Highway(activation = 'relu'))
model.add(Dropout(0.3))
model.add(Dense(4))
model.add(Activation('softmax'))
print 'Compiling model...'
model.compile('adam', 'categorical_crossentropy')
model.summary()
print 'Training:'
try:
model.fit(X_train, y_train, batch_size=512,
callbacks = [
EarlyStopping(verbose=True, patience=20, monitor='val_loss'),
ModelCheckpoint(MODEL_FILE + RUN_NAME +'-progress', monitor='val_loss', verbose=True, save_best_only=True)
],
nb_epoch=100,
validation_split = 0.2,
show_accuracy=True)
except KeyboardInterrupt:
print 'Training ended early.'
# -- load in best network
model.load_weights(MODEL_FILE + RUN_NAME +'-progress')
if (SAVE_PROTOBUF):
print 'Saving protobuf'
# write out to a new directory called models
# the actual graph file is graph.pb
# the graph def is in the global session
import tensorflow as tf
import keras.backend.tensorflow_backend as tfbe
sess = tfbe._SESSION
saver = tf.train.Saver()
tf.train.write_graph(sess.graph_def, 'models/', 'graph.pb', as_text=False)
save_path = saver.save(sess, "./model-weights.ckpt")
print "Model saved in file: %s" % save_path
print saver.as_saver_def().filename_tensor_name
print saver.as_saver_def().restore_op_name
print model.get_output()
print 'Saving weights...'
model.save_weights('./weights/ip3d-replacement_' + MODEL_FILE + RUN_NAME +'.h5', overwrite=True)
json_string = model.to_json()
open(MODEL_FILE + RUN_NAME +'.json', 'w').write(json_string)
print 'Testing...'
yhat = model.predict(X_test, verbose = True, batch_size = 512)
io.save('yhat'+ RUN_NAME +'.h5', yhat)
print 'Plotting ROC...'
fg = plot_ROC(y_test, yhat, ip3d, MODEL_FILE)
#plt.show()
fg.savefig('./plots/roc' + MODEL_FILE + RUN_NAME +'.pdf')
def columbia_net(shape=(64, 64), nb_channels=1):
logger.info('generating net with input shape ({})'.format(', '.join(
str(s) for s in shape)))
img_width, img_height = shape
nb_poses = 5
nb_vertical = 3
nb_horiz = 7
face = Input(shape=(nb_channels, img_width, img_height))
left_eye = Input(shape=(nb_channels, img_width, img_height))
right_eye = Input(shape=(nb_channels, img_width, img_height))
face_model = Sequential()
face_model.add(Flatten(input_shape=(nb_channels, img_width, img_height)))
face_model.add(Dense(1024, activation='relu'))
face_model.add(Dropout(0.25))
face_model.add(Highway(activation='relu'))
face_model.add(Dropout(0.25))
face_model.add(Highway(activation='relu'))
face_model.add(Dropout(0.25))
face_model.add(Highway(activation='relu'))
face_model.add(Dense(512, activation='relu'))
face_h = face_model(face)
eye_model = Sequential()
eye_model.add(Flatten(input_shape=(nb_channels, img_width, img_height)))
eye_model.add(Dense(1024, activation='relu'))
eye_model.add(Dropout(0.25))
eye_model.add(Highway(activation='relu'))
eye_model.add(Dropout(0.25))
eye_model.add(Highway(activation='relu'))
eye_model.add(Dropout(0.25))
eye_model.add(Highway(activation='relu'))
eye_model.add(Dense(512, activation='relu'))
# eye_model.add(Flatten())
left_eye_h = eye_model(left_eye)
right_eye_h = eye_model(right_eye)
# combined = merge([face_h, left_eye_h, right_eye_h], mode='concat', concat_axis=1)
eyes = merge([left_eye_h, right_eye_h], mode='sum')
combined = merge([face_h, eyes], mode='concat', concat_axis=1)
h = Dense(128)(combined)
h = Activation('relu')(h)
h = Dropout(0.2)(h)
out_pose = Dense(nb_poses, activation='softmax', name='pose')(h)
h = Dense(128)(combined)
h = Activation('relu')(h)
h = Dropout(0.2)(h)
out_vertical = Dense(nb_vertical, activation='softmax', name='vertical')(h)
h = Dense(128)(combined)
h = Activation('relu')(h)
h = Dropout(0.2)(h)
out_horiz = Dense(nb_horiz, activation='softmax', name='horizontal')(h)
model = Model(input=[face, left_eye, right_eye],
output=[out_pose, out_vertical, out_horiz])
logger.info('compiling with Adam and mse')
model.compile('adam',
3 * ['sparse_categorical_crossentropy'],
metrics=['acc'])
return model
def LSTMCNN(opt):
# opt.seq_length = number of time steps (words) in each batch
# opt.rnn_size = dimensionality of hidden layers
# opt.num_layers = number of layers
# opt.dropout = dropout probability
# opt.word_vocab_size = num words in the vocab
# opt.word_vec_size = dimensionality of word embeddings
# opt.char_vocab_size = num chars in the character vocab
# opt.char_vec_size = dimensionality of char embeddings
# opt.feature_maps = table of feature map sizes for each kernel width
# opt.kernels = table of kernel widths
# opt.length = max length of a word
# opt.use_words = 1 if use word embeddings, otherwise not
# opt.use_chars = 1 if use char embeddings, otherwise not
# opt.highway_layers = number of highway layers to use, if any
# opt.batch_size = number of sequences in each batch
if opt.use_words:
word = Input(batch_shape=(opt.batch_size, opt.seq_length),
dtype='int32',
name='word')
word_vecs = Embedding(opt.word_vocab_size,
opt.word_vec_size,
input_length=opt.seq_length)(word)
if opt.use_chars:
chars = Input(batch_shape=(opt.batch_size, opt.seq_length,
opt.max_word_l),
dtype='int32',
name='chars')
chars_embedding = TimeDistributed(
Embedding(opt.char_vocab_size,
opt.char_vec_size,
name='chars_embedding'))(chars)
cnn = CNN(opt.seq_length, opt.max_word_l, opt.char_vec_size,
opt.feature_maps, opt.kernels, chars_embedding)
if opt.use_words:
x = Merge(mode='concat')([cnn, word_vecs])
inputs = [chars, word]
else:
x = cnn
inputs = chars
else:
x = word_vecs
inputs = word
if opt.batch_norm:
x = BatchNormalization()(x)
for l in range(opt.highway_layers):
x = TimeDistributed(Highway(activation='relu'))(x)
for l in range(opt.num_layers):
x = LSTM(opt.rnn_size,
activation='tanh',
inner_activation='sigmoid',
return_sequences=True,
stateful=True)(x)
if opt.dropout > 0:
x = Dropout(opt.dropout)(x)
output = TimeDistributed(Dense(opt.word_vocab_size,
activation='softmax'))(x)
model = sModel(input=inputs, output=output)
print model.summary()
optimizer = sSGD(lr=opt.learning_rate,
clipnorm=opt.max_grad_norm,
scale=float(opt.seq_length))
model.compile(loss='sparse_categorical_crossentropy', optimizer=optimizer)
return model
model.add(merged)
# model.add(BatchNormalization())
# multiply passage by the dot product
# add softmax here
# model.add(Dropout(.5))
# model.add(Dense(100, activation='softmax'))
# model.add(MaxPooling1D(pool_length=4, stride=None, border_mode='valid'))
# model.add(Activation('relu'))
# model.add(Permute((2, 1)))
# model.add(AveragePooling1D(pool_length=5, stride=None, border_mode='valid'))
# model.add(MaxPooling1D(pool_length=MAX_QUESTION_LENGTH/5, stride=None, border_mode='valid'))
# model.add(Permute((2, 1)))
model.add(Flatten())
model.add(Dropout(.2))
model.add(Highway()) # looks like this kind of worked
model.add(Dropout(.2))
model.add(Dense(MAX_PASSAGE_LENGTH, activation='softmax'))
plot(model, to_file='model.png', show_shapes=True)
# train a 1D convnet with global maxpooling
# adam = Adam(lr=.0001, clipnorm=10)
model.compile(loss='categorical_crossentropy',
optimizer='adam',
metrics=['acc', 'recall'])
# metrics=['recall'])
# happy learning!
# model.fit(x=[passages, questions], y=labels, nb_epoch=2, batch_size=128)
model.fit([p_train, q_train],
(X_train, y_train), (X_test, y_test) = mnist.load_data()
X_train = X_train.reshape(60000, img_channels, img_rows, img_cols)
X_test = X_test.reshape(10000, img_channels, img_rows, img_cols)
X_train = X_train.astype('float32')
X_test = X_test.astype('float32')
X_train /= 255
X_test /= 255
Y_train = np_utils.to_categorical(y_train, nb_classes)
Y_test = np_utils.to_categorical(y_test, nb_classes)
inp_img = Input(shape=(
img_channels,
img_rows,
img_cols,
))
x = Flatten()(inp_img)
x = Dense(32, activation='relu')(x)
for _ in range(nb_layer):
x = Highway(activation='relu')(x)
y = Dense(nb_classes, activation='softmax')(x)
classifier = Model(input=inp_img, output=y)
classifier.compile(optimizer='rmsprop',
loss='categorical_crossentropy',
metrics=['accuracy'])
classifier.fit(X_train,
Y_train,
nb_epoch=100,
batch_size=batch_size,
verbose=1)
def build_GRU(dim, lsize, bsize, cost = 'mse'):
def filt(args):
return subsample(args[0]) * K.abs(subsample(args[1]))
def subtract(args):
return K.abs(args[0] - args[1])
def myfun(args):
return K.abs(subsample(args[0]))
def rect(args):
return K.abs(args[0])
def flipsum(args):
return args[0][:, ::-1, :] + args[1]
def subsample(args):
return args[:, 3:15, :]
xin = Input(batch_shape = (bsize, lsize, dim))
# Encoding Part using bi-directional GRUs
rnnAF = GRU(dim, init='glorot_normal', activation = 'tanh',
inner_activation='hard_sigmoid', W_regularizer=None,
U_regularizer=None, stateful = False, return_sequences = True, consume_less = 'gpu') (xin)
mr = merge([xin, rnnAF], mode = 'sum')
rnnBF = GRU(dim, init='glorot_normal', activation = 'tanh',
inner_activation='hard_sigmoid', W_regularizer=None,
U_regularizer=None, go_backwards = True, return_sequences = True, consume_less = 'mem') (xin)
mrB = merge([xin, rnnBF], mode = flipsum, output_shape = (lsize, dim))
# The return of Bi-GRUs
mrBDIR = merge([mr, mrB], mode = 'concat')
# Decoding part
rnnDsv = GRU(dim, init='glorot_normal', activation = 'tanh',
inner_activation='hard_sigmoid', W_regularizer=None, U_regularizer=None,
stateful = False, return_sequences = True, consume_less = 'gpu') (mrBDIR)
svout = merge([xin, rnnDsv], output_shape = (lsize/2, dim), mode = filt)
# Post filtering with sparsity constraint
hC = TimeDistributed(Highway(input_dim = dim, activation='relu', activity_regularizer=activity_l2(1e-4))) (svout)
model = Model(xin, [svout, hC, rnnDsv])
# Cost Functions
def mseloss(ytrue, ypred):
# Update of the true
ytrue = xin * (K.pow(ytrue, 1.) + K.epsilon())/(K.pow(xin, 1.) + K.epsilon())
return K.sum(K.pow(ytrue-ypred,2.), axis = -1)
def KL(ytrue, ypred):
# Update of the true
ytrue = subsample(xin) * (K.pow(ytrue, 1.) + K.epsilon())/(K.pow(subsample(xin), 1.) + K.epsilon()) + 1e-6
ypred += 1e-6
return K.sum(ytrue * (K.log(ytrue) - K.log(ypred)) + (ypred - ytrue), axis=-1)
def KLbkg(ytrue, ypred):
# Update of the true
ytrue = subsample(xin) * K.abs(1. - ((K.pow(ytrue, 1.) + K.epsilon())/(K.pow(subsample(xin), 1.) + K.epsilon()))) + 1e-6
ypred += 1e-6
return K.sum(ytrue * (K.log(ytrue) - K.log(ypred)) + (ypred - ytrue), axis=-1)
if cost == 'mse':
print('MSE')
model.compile(optimizer = opt, loss = [mseloss, mseloss])
elif cost == 'kl':
print('Kullback-Leibler')
model.compile(optimizer = opt, loss = [KL, KL])
elif cost == 'klbkg':
print('Kullback-Leibler for Accompaniment Instrument')
model.compile(optimizer = opt, loss = [KLbkg, KLbkg])
return model
model_left.add(Dense(5, input_dim=4, init='glorot_uniform'))
model_left.add(BatchNormalization(mode=2))
model_left.add(Activation('relu'))
model_left.add(Dense(5))
model_left.add(BatchNormalization(mode=2))
model_left.add(Activation('relu'))
model_left.add(Dense(3))
model_left.add(Activation('relu'))
model_left.add(Dense(4))
for i in range(0, 6):
print(i, model_left.layers[i].name)
model_right = Sequential()
model_right.add(Dense(4, input_shape=(4, )))
model_right.add(Highway())
model_right.add(BatchNormalization(mode=2))
model2 = Sequential()
model2.add(Merge([model_left, model_right], mode='concat'))
model2.add(Activation('relu'))
model2.add(Reshape((8, )))
model2.add(Dense(5))
model2.add(BatchNormalization(mode=2))
model2.add(Activation('relu'))
model2.add(Dense(3))
model2.add(Activation('relu'))
model2.add(Dense(4))
for i in range(0, 6):
print(i, model2.layers[i].name)
def __init__(self,
n_in,
hidden_layer_size,
n_out,
L1_reg,
L2_reg,
hidden_layer_type,
output_type='LINEAR',
dropout_rate=0.0):
logger = logging.getLogger("DNN initialization")
self.n_in = int(n_in)
self.n_out = int(n_out)
self.n_layers = len(hidden_layer_size)
self.dropout_rate = dropout_rate
self.L1_reg = L1_reg
self.L2_reg = L2_reg
self.optimizer = 'adam'
# fix random seed for reproducibility
seed = 123
np.random.seed(seed=seed)
# Model must have atleast one hidden layer
assert self.n_layers > 0, 'Model must have at least one hidden layer'
# Number of hidden layers and their types should be equal
assert len(hidden_layer_size) == len(hidden_layer_type)
### Create model graph ###
self.model = Sequential()
self.model.add(
Dense(output_dim=128,
input_dim=n_in,
init='glorot_uniform',
activation='relu',
W_regularizer=l1l2(l1=self.L1_reg, l2=self.L2_reg)))
self.model.add(Dropout(self.dropout_rate))
num_layers = 15
for i in xrange(num_layers):
self.model.add(Highway(activation='relu'))
self.model.add(Dropout(self.dropout_rate))
#self.model.add(Dropout(dropout))
# add output layer
if output_type.lower() == 'linear':
self.final_layer = self.model.add(
Dense(output_dim=n_out,
input_dim=hidden_layer_size[-1],
init='glorot_uniform',
activation='linear',
W_regularizer=l1l2(l1=self.L1_reg, l2=self.L2_reg)))
elif output_type.lower() == 'sigmoid':
self.final_layer = self.model.add(
Dense(output_dim=n_out,
input_dim=hidden_layer_size[-1],
init='glorot_uniform',
activation='sigmoid',
W_regularizer=l1l2(l1=self.L1_reg, l2=self.L2_reg)))
else:
logger.critical(
"This output activation function: %s is not supported right now!"
% (output_type))
sys.exit(1)
# Compile the model
self.model.compile(loss='mse', optimizer=self.optimizer)
data=pd.DataFrame(np.concatenate((X_train2,Y_train),axis=1))
data2=shuffle(data)
X_train2=np.array(data2.ix[:,0:3])
Y_train=np.array(pd.get_dummies(data2.ix[:,4]))
sgd = SGD(lr=learning_rate,momentum=momentum, decay=decay_rate, nesterov=False)
np.var(X_train2.T)
model = Sequential()
model.add(Dense(7, input_dim=4, init='glorot_uniform'))
model.add(Activation('relu'))
model.add(Dense(7, init='glorot_uniform'))
model.add(Highway())
model.add(Dense(3, init='glorot_uniform'))
model.add(Activation('sigmoid'))
model.compile(loss='categorical_crossentropy',
optimizer=sgd,metrics=['accuracy'])
model.fit(X_train2, Y_train,
batch_size = 30, nb_epoch = 1000, verbose = 1,validation_split=0.9)
res22 = model.predict_classes([X_train2,X_train2,X_train2],batch_size = 30)
acc22=((res22-data2.ix[:,4])==0).sum()/len(res22)
acc22
# Note, there is a bug in original implementation of TimeDistributed in keras
input_tensor = tf.placeholder(tf.int32, shape=(
opts.batch_size, opts.sequence_length))
a = Input(batch_shape=(opts.batch_size, opts.sequence_length),
tensor=input_tensor, name='input')
e = Embedding(opts.vocabulary_size,
opts.embedding_size,
input_length=opts.sequence_length, name='embedding')(a)
x = e
for i in range(1, opts.interaction_times + 1):
x = PairwiseInteraction(gate_type=opts.gate_type,
activation=opts.activation_type,
dropout=opts.dropout,
name='interaction layer %d' % i)(x)
if opts.highway:
x = TimeDistributed(Highway())(x)
x = KMaxTensorPooling(opts.sequence_length,
name='kmaxpooling layer %d' % i)(x)
x = Lambda(lambda t: tf.reshape(
t, [-1, opts.sequence_length * opts.embedding_size]))(x)
e = Lambda(lambda t: tf.reshape(
t, [-1, opts.sequence_length * opts.embedding_size]))(e)
x = merge([x, e], mode='concat', concat_axis=1)
x = Dense(128, activation='sigmoid')(x)
x = Dense(32, activation='sigmoid')(x)
x = Dense(1, activation='sigmoid', name='prob')(x)
model = Model(input=a, output=x)
model.compile(optimizer='nadam',
loss='binary_crossentropy',
metrics=['accuracy'])
def _generate_model(self, lembedding, num_classes=2, ngrams=[1,2,3,4,5],
nfilters=64, rnn_type=GRU, rnn_dim=80, train_vectors=True):
CHARACTERS_PER_WORD = lembedding.size_level1
WORDS_PER_DOCUMENT = lembedding.size_level2
EMBEDDING_DIM = lembedding.vector_box.vector_dim
INPUT_SHAPE = (CHARACTERS_PER_WORD * WORDS_PER_DOCUMENT, )
EMBEDDING_SHAPE = (WORDS_PER_DOCUMENT, CHARACTERS_PER_WORD, EMBEDDING_DIM)
doc = Input(shape=(INPUT_SHAPE[0], ), dtype='int32')
embedded = Sequential([
Embedding(
input_dim=lembedding.vector_box.size,
output_dim=EMBEDDING_DIM,
input_length=INPUT_SHAPE[0]
),
Reshape(EMBEDDING_SHAPE)
])(doc)
def sub_model(n):
return Sequential([
Convolution1D(nfilters, n,
activation='relu',
input_shape=EMBEDDING_SHAPE[1:]
),
Lambda(
lambda x: K.max(x, axis=1),
output_shape=(nfilters,)
)
])
rep = Dropout(0.5)(
merge(
[TimeDistributed(sub_model(n))(embedded) for n in ngrams],
mode='concat',
concat_axis=-1
)
)
out = Dropout(0.5)(
merge(
[rnn_type(rnn_dim)(rep), rnn_type(rnn_dim, go_backwards=True)(rep)],
mode='concat',
concat_axis=-1
)
)
mapping = [
Highway(activation='relu'),
Dropout(0.5),
Dense(64, activation='relu'),
Dropout(0.4)
]
for f in mapping:
out = f(out)
if num_classes == 2:
out = Dense(1, activation='sigmoid')(out)
model = Model(input=doc, output=out)
if self.optimizer is None:
self.optimizer = 'rmsprop'
model.compile(loss='binary_crossentropy', optimizer=self.optimizer, metrics=["accuracy"])
else:
out = Dense(num_classes, activation='softmax')(out)
model = Model(input=doc, output=out)
if self.optimizer is None:
self.optimizer = 'adam'
model.compile(loss='categorical_crossentropy', optimizer=self.optimizer, metrics=["accuracy"])
return model
return (x-np.min(x))/(np.max(x)-np.min(x))
part=8
thre=1
## Certo é 256
recog=Sequential()
recog.add(Dense(64,activation='relu',input_shape=(784,),init='glorot_uniform'))
recog_left=recog
recog_left.add(Dense(64,input_shape=(64,),activation='relu'))
recog_right=recog
recog_right.add(Dense(64,input_shape=(64,),activation='relu'))
recog_right.add(Lambda(lambda x: x + K.exp(x / 2) * K.random_normal(shape=(1, 64), mean=0.,
std=epsilon_std), output_shape=(64,)))
recog_right.add(Highway())
recog_right.add(Activation('sigmoid'))
recog1=Sequential()
recog1.add(Merge([recog_left,recog_right],mode = 'ave'))
recog1.add(Dense(784))
#### HERE***
recog11=Sequential()
layer=Dense(64,init='glorot_uniform',input_shape=(784,))
layer.trainable=False
recog11.add(layer)
layer2=Dense(784, activation='sigmoid',init='glorot_uniform')
layer2.trainable=False
recog11.add(layer2)
recog11.layers[0].W.set_value(np.ones((784,64)).astype(np.float32))
def main(MODEL_FILE):
test_dict = io.load('./data/test_dict_IPConv.h5')
train_dict = io.load('./data/train_dict_IPConv.h5')
X_train = train_dict['X']
y_train = train_dict['y']
n_features = X_train.shape[2]
X_test = test_dict['X']
y_test = test_dict['y']
ip3d = test_dict['ip3d'] # this is a df
print 'Building model...'
if (MODEL_FILE == 'CRNN'):
graph = build_graph(n_features)
model = Sequential()
model.add(graph)
model.add(Dense(64))
elif (MODEL_FILE == 'RNN'):
graph = build_graph_noCNN(n_features)
model = Sequential()
model.add(graph)
model.add(Dense(64))
model.add(Dropout(0.4))
model.add(Highway(activation='relu'))
model.add(Dropout(0.4)) #3
model.add(Dense(4))
model.add(Activation('softmax'))
print 'Compiling model...'
model.compile('adam', 'categorical_crossentropy')
model.summary()
print 'Training:'
try:
model.fit(X_train,
y_train,
batch_size=512,
callbacks=[
EarlyStopping(verbose=True,
patience=20,
monitor='val_loss'),
ModelCheckpoint(MODEL_FILE + '-progress',
monitor='val_loss',
verbose=True,
save_best_only=True)
],
nb_epoch=200,
validation_split=0.2,
show_accuracy=True)
except KeyboardInterrupt:
print 'Training ended early.'
# -- load in best network
model.load_weights(MODEL_FILE + '-progress')
print 'Saving weights...'
model.save_weights('./weights/ip3d-replacement_' + MODEL_FILE + '.h5',
overwrite=True)
print 'Testing...'
yhat = model.predict(X_test, verbose=True, batch_size=512)
print 'Plotting ROC...'
fg = plot_ROC(y_test, yhat, ip3d, MODEL_FILE)
#plt.show()
fg.savefig('./plots/roc_' + MODEL_FILE + '.pdf')
