def test_testing_convolution(text_vectorizer, class_vectorizer, classif_level, classif_type, dataset_location):
load_standard_sets = True
root_location = fh.get_root_location('data/convolutional_outcome/')
sets_location = fh.join_paths(root_location, "model_sets")
checkpoint_path = fh.join_paths(root_location, "model_checkpoints")
model_path = fh.link_paths(checkpoint_path, 'convolution_model')
weights_path = fh.link_paths(checkpoint_path, 'convolution_weights')
# load sets and settings
model_name = text_vectorizer+'/'+class_vectorizer+'/NN'
# train_data, test_data, val_data, train_labels, test_labels, val_labels, settings = ch.load_sets(sets_location, '2020-01-08T22:28:44.757410')
# classes, n_classes, vocab_processor, len_vocabulary = settings
# only for test
train_data, test_data, val_data, train_labels, test_labels, val_labels, _ = ch.load_sets(sets_location)
# it could be that a label is only in the test/data data, might be a problem
sequence_length = train_data.shape[1]
# calculates metrics with testing data
model, predictions = pmh.run_cnn_test(_,
train_data, train_labels, test_data, test_labels, val_data, val_labels,
model_path, weights_path, False)
binary_predictions = mh.get_binary_0_5(predictions)
# display testing metrics
metrics = mh.get_sequential_metrics(test_labels, predictions, binary_predictions)
mh.display_sequential_metrics('testing convolution sequence', metrics)
classifier_name, layers = ch.get_sequential_classifier_information(model)
ch.save_results(classifier_name+' Test', metrics, layers, model_name, classif_level, classif_type, dataset_location)
def test_basic_LSTM(data_frame, text_vectorizer, classif_level, classif_type, dataset_location):
print('### LSTM Doing Testing ###')
root_location = fh.get_root_location('data/lstm_outcome/')
doc2vec_model_location = fh.link_paths(fh.join_paths(root_location, "doc2vec_model/vocab_model/"), "doc2vec_model")
model_location = link_paths(join_paths(root_location, "lstm_model"), "lstm_model")
sets_location = join_paths(root_location, "model_sets")
save_results = True
load_model = True
# date problem
X_train, X_test, X_val, y_train, y_test, y_val, settings = load_sets(sets_location)
classes, n_classes, vocab_processor, len_vocabulary = settings
training_docs_list = X_train['patent_id']
test_docs_list = X_test['patent_id']
val_docs_list = X_val['patent_id']
sequence_size, embedding_size = 1, 150
X_data, Xv_data, Xt_data = ch.get_df_data(3, training_docs_list, val_docs_list, test_docs_list, sequence_size, embedding_size, doc2vec_model_location)
#####
print(X_data.shape)
input_size, output_size = X_data.shape[2], n_classes
if load_model:
model = tf.keras.models.load_model(model_location)
min_delta, patience = 0.00001, 15
early_stopper = keras.callbacks.EarlyStopping(monitor='val_loss', min_delta=min_delta, patience=patience, verbose=1, mode='auto')
metrics_callback = MetricsCNNCallback(Xv_data, y_val, patience)
metrics, predictions, val_metrics = run_lstm(model,
X_data, y_train, Xt_data, y_test, Xv_data, y_val,
batch_size, [early_stopper, metrics_callback], queue_size,
training_docs_list, val_docs_list)
binary_predictions = mh.get_binary_0_5(predictions)
print('\nGenerating Testing Metrics')
metrics = mh.get_sequential_metrics(y_val, predictions, binary_predictions)
mh.display_sequential_metrics(metrics)
if save_results:
classifier_name, parameters = ch.get_sequential_classifier_information(model)
model_name = text_vectorizer+'/'+class_vectorizer+'/'+classifier_name
ch.save_results(classifier_name+'_LSTM', metrics, parameters, model_name, classif_level, classif_type, dataset_location)
print('end testing step')
def train_testing_convolution(data_frame, text_vectorizer, class_vectorizer):
save_standard_sets = True
root_location = fh.get_root_location('data/convolutional_outcome/')
sets_location = fh.join_paths(root_location, "model_sets")
checkpoint_path = fh.join_paths(root_location, "model_checkpoints")
model_path = fh.link_paths(checkpoint_path, 'convolution_model')
weights_path = fh.link_paths(checkpoint_path, 'convolution_weights')
# get sets
model_name = text_vectorizer+'/'+class_vectorizer+'/NN'
standard_results = ch.apply_df_vectorizer(data_frame, text_vectorizer, class_vectorizer, model_name)
train_data, test_data, train_labels, test_labels, classes, n_classes, vocab_processor, len_vocabulary = standard_results
train_data, val_data, train_labels, val_labels = ch.get_train_test_from_data(train_data, train_labels)
# save sets
# ch.save_sets(sets_location, train_data, test_data, val_data, train_labels, test_labels, val_labels,
# [classes, n_classes, vocab_processor, len_vocabulary])
# this is for test
train_data, test_data, val_data, train_labels, test_labels, val_labels, _ = ch.load_sets(sets_location)
# it could be that a label is only in the test/data data, might be a problem
sequence_length = train_data.shape[1]
# define the model
model = pmh.get_cnn_test(len_vocabulary, n_classes, sequence_length)
# calculates metrics with validating data
model, val_predictions = pmh.run_cnn_test(model,
train_data, train_labels, val_data, val_labels, val_data, val_labels,
model_path, weights_path, True)
binary_val_predictions = mh.get_binary_0_5(val_predictions)
print(val_labels.shape)
print(val_predictions.shape)
# display validation metrics
metrics = mh.get_sequential_metrics(val_labels, val_predictions, binary_predictions)
mh.display_sequential_metrics('validation convolution sequence', metrics)
def test_LSTM_from_web(data_frame, text_vectorizer, class_vectorizer, classif_level, classif_type, dataset_location):
print('### LSTM Doing Testing ###')
root_location = fh.get_root_location('data/lstm_outcome/')
nn_parameter_search_location = fh.join_paths(root_location, "nn_fhv_parameter_search")
doc2vec_model_location = fh.link_paths(fh.join_paths(root_location, "doc2vec_model/vocab_model/"), "doc2vec_model")
save_results = True
sequence_size = 1
EMBEDDING_SIZE = 150
model_name = text_vectorizer+'/'+class_vectorizer+'/LSTM'
results = ch.apply_df_vectorizer(data_frame, text_vectorizer, class_vectorizer, model_name)
X_train, X_test, y_train, y_test, classes, n_classes, vocab_processor, len_vocabulary = results
X_train, X_val, y_train, y_val = ch.get_train_test_from_data(X_train, y_train)
training_docs_list = X_train['patent_id']
test_docs_list = X_test['patent_id']
val_docs_list = X_val['patent_id']
X_data, Xv_data, Xt_data = ch.get_df_data(3, training_docs_list, val_docs_list, test_docs_list, sequence_size, EMBEDDING_SIZE, doc2vec_model_location)
GLOBAL_VARS.DOC2VEC_MODEL_NAME, GLOBAL_VARS.MODEL_NAME = wmh.set_parameters_lstm_doc2vec(nn_parameter_search_location, classif_level, classif_type)
NN_INPUT_NEURONS, NN_SEQUENCE_SIZE, NN_OUTPUT_NEURONS = pmh.get_lstm_shapes(X_data, n_classes)
NN_BATCH_SIZE, PARTS_LEVEL, NN_MAX_EPOCHS, QUEUE_SIZE = pmh.get_lstm_basic_parameters()
params = pmh.get_lstm_testing_parameters()
lstm_output_size,w_dropout_do,u_dropout_do, stack_layers, conv_size, conv_filter_length, conv_max_pooling_length = params
EARLY_STOPPER_MIN_DELTA, EARLY_STOPPER_PATIENCE = pmh.get_early_stopping_parameters()
TEST_METRICS_FILENAME = '{}_level_{}_standard_nn_test_metrics_dict.pkl'
test_metrics_dict = dict()
test_metrics_path = fh.link_paths(fh.link_paths(nn_parameter_search_location, GLOBAL_VARS.MODEL_NAME), TEST_METRICS_FILENAME.format(classif_type, PARTS_LEVEL))
param_results_path = fh.link_paths(fh.link_paths(nn_parameter_search_location, GLOBAL_VARS.MODEL_NAME), NN_PARAMETER_SEARCH_PREFIX.format(classif_type, classif_level, NN_BATCH_SIZE))
param_results_dict = pickle.load(open(param_results_path, 'rb'))
GLOBAL_VARS.NN_MODEL_NAME = 'lstm_size_{}_w-drop_{}_u-drop_{}_stack_{}_conv_{}'.format(lstm_output_size,
w_dropout_do,
u_dropout_do,
stack_layers,
str(conv_size)
)
if conv_size:
GLOBAL_VARS.NN_MODEL_NAME += '_conv-filter-length_{}_max-pooling-size_{}'.format(conv_filter_length,
conv_max_pooling_length)
if GLOBAL_VARS.NN_MODEL_NAME not in param_results_dict.keys():
print("Can't find model: {}".format(GLOBAL_VARS.NN_MODEL_NAME))
raise Exception()
if fh.ensure_exists_path_location(test_metrics_path):
test_metrics_dict = pickle.load(open(fh.link_paths(fh.link_paths(nn_parameter_search_location, GLOBAL_VARS.MODEL_NAME), TEST_METRICS_FILENAME.format(classif_type,PARTS_LEVEL)), 'rb'))
if GLOBAL_VARS.NN_MODEL_NAME in test_metrics_dict.keys():
print("Test metrics already exist for: {}".format(GLOBAL_VARS.NN_MODEL_NAME))
test_metrics = test_metrics_dict[GLOBAL_VARS.NN_MODEL_NAME]
print("** Test Metrics: Cov Err: {:.3f}, Avg Labels: {:.3f}, \n\t\t Top 1: {:.3f}, Top 3: {:.3f}, Top 5: {:.3f}, \n\t\t F1 Micro: {:.3f}, F1 Macro: {:.3f}".format(
test_metrics['coverage_error'], test_metrics['average_num_of_labels'],
test_metrics['top_1'], test_metrics['top_3'], test_metrics['top_5'],
test_metrics['f1_micro'], test_metrics['f1_macro']))
raise Exception()
print('***************************************************************************************')
print(GLOBAL_VARS.NN_MODEL_NAME)
model = pmh.get_keras_rnn_model(NN_INPUT_NEURONS, NN_SEQUENCE_SIZE, NN_OUTPUT_NEURONS,
lstm_output_size, w_dropout_do, u_dropout_do, stack_layers, conv_size,
conv_filter_length, conv_max_pooling_length)
# get model best weights
weights = param_results_dict[GLOBAL_VARS.NN_MODEL_NAME]['best_weights']
model.set_weights(weights)
print('Evaluating on Test Data using best weights')
_, ytp, ytp_binary = pmh.predict_generator(None, model, Xt_data, y_test, NN_BATCH_SIZE, QUEUE_SIZE, test_docs_list)
print('Generating Test Metrics')
test_metrics = mh.get_sequential_metrics(y_test, ytp, ytp_binary)
mh.display_sequential_metrics(test_metrics)
if save_results:
classifier_name, parameters = ch.get_sequential_classifier_information(model)
ch.save_results(classifier_name+'_LSTM', test_metrics, parameters, model_name, classif_level, classif_type, dataset_location)
test_metrics_dict[GLOBAL_VARS.NN_MODEL_NAME] = test_metrics
pickle.dump(test_metrics_dict, open(test_metrics_path, 'wb'))
def train_LSTM_from_web(data_frame, text_vectorizer, class_vectorizer, classif_level, classif_type, dataset_location):
print('### LSTM Doing Training ###')
root_location = fh.get_root_location('data/lstm_outcome/')
exports_location = fh.join_paths(root_location, "exported_data/")
matrices_save_location = fh.join_paths(root_location, "fhv_matrices/")
nn_parameter_search_location = fh.join_paths(root_location, "nn_fhv_parameter_search")
doc2vec_model_location = fh.link_paths(fh.join_paths(root_location, "doc2vec_model/vocab_model/"), "doc2vec_model")
load_existing_results = True # it was True
save_results = True
sequence_size = 1
EMBEDDING_SIZE = 150
model_name = text_vectorizer+'/'+class_vectorizer+'/LSTM'
results = ch.apply_df_vectorizer(data_frame, text_vectorizer, class_vectorizer, model_name)
X_train, X_val, y_train, y_val, classes, n_classes, vocab_processor, len_vocabulary = results
training_docs_list = X_train['patent_id']
val_docs_list = X_val['patent_id']
X_data, Xv_data, _ = ch.get_df_data(2, training_docs_list, val_docs_list, None, sequence_size, EMBEDDING_SIZE, doc2vec_model_location)
GLOBAL_VARS.DOC2VEC_MODEL_NAME, GLOBAL_VARS.MODEL_NAME = wmh.set_parameters_lstm_doc2vec(nn_parameter_search_location, classif_level, classif_type)
# print(X_data.shape) # 64, 1, 200
# print(Xt_data.shape) # 20, 1, 200
# print(Xv_data.shape) # 16, 1, 200
NN_INPUT_NEURONS, NN_SEQUENCE_SIZE, NN_OUTPUT_NEURONS = pmh.get_lstm_shapes(X_data, n_classes)
NN_BATCH_SIZE, PARTS_LEVEL, NN_MAX_EPOCHS, QUEUE_SIZE = pmh.get_lstm_basic_parameters()
param_sampler = pmh.get_lstm_training_parameters()
EARLY_STOPPER_MIN_DELTA, EARLY_STOPPER_PATIENCE = pmh.get_early_stopping_parameters()
param_results_dict = dict()
param_results_path = fh.link_paths(fh.join_paths(nn_parameter_search_location, GLOBAL_VARS.MODEL_NAME), NN_PARAMETER_SEARCH_PREFIX.format(classif_type, classif_level, NN_BATCH_SIZE))
index = param_results_path.rfind('/')
fh.create_folder(fh.link_paths(fh.join_paths(nn_parameter_search_location, GLOBAL_VARS.MODEL_NAME), NN_PARAMETER_SEARCH_PREFIX.format(classif_type, classif_level, NN_BATCH_SIZE))[:index])
###########
# print(X_data.shape)
# input_size, sequence_size, output_size = X_data.shape[2], X_data.shape[0], n_classes
# NN_BATCH_SIZE, PARTS_LEVEL, NN_MAX_EPOCHS, QUEUE_SIZE = 1, 2, 200, 100
# lstm_output_sizes = [500, 1000]
# w_dropout_options = [0., 0.5]
# u_dropout_options = [0., 0.5]
# stack_layers_options = [1, 2, 3]
# lstm_output_size, w_dropout_do, u_dropout_do, stack_layers = 500, 0.0, 0.0, 1
# conv_size, conv_filter_length, max_pooling_length = 128, 2, 2
# EARLY_STOPPER_MIN_DELTA, EARLY_STOPPER_PATIENCE = 0.00001, 15
# import tensorflow as tf
# from tensorflow import keras
# from keras.layers import Input, Dense, Dropout, Activation
# from keras.models import Model, Sequential
# from keras.layers.convolutional import MaxPooling1D, Convolution1D
# from keras.layers.recurrent import LSTM
# model = Sequential()
# # model.add(Convolution1D(nb_filter=conv_size, input_shape=(sequence_size, input_size),
# # filter_length=conv_filter_length, border_mode='same', activation='relu'))
# # model.add(MaxPooling1D(pool_length=max_pooling_length))
# model.add(LSTM(lstm_output_size, activation='tanh', recurrent_activation='hard_sigmoid', use_bias=False,
# input_dim=input_size, dropout_W=w_dropout_do, dropout_U=u_dropout_do,
# implementation=1,
# return_sequences=False if 1 == stack_layers else True,
# go_backwards=False, stateful=False, unroll=False,
# name='lstm_{}_w-drop_{}_u-drop_{}_layer_{}'.format(lstm_output_size, str(u_dropout_do),
# str(w_dropout_do), str(1))))
# # model.add(LSTM(lstm_output_size, activation='tanh', recurrent_activation='hard_sigmoid', use_bias=False,
# # input_dim=input_size, dropout_W=w_dropout_do, dropout_U=u_dropout_do,
# # implementation=1,
# # return_sequences=False if 2 == stack_layers else True,
# # go_backwards=False, stateful=False, unroll=False,
# # name='lstm_{}_w-drop_{}_u-drop_{}_layer_{}'.format(lstm_output_size, str(u_dropout_do),
# # str(w_dropout_do), str(2))))
# # model.add(LSTM(lstm_output_size, activation='tanh', recurrent_activation='hard_sigmoid', use_bias=False,
# # input_dim=input_size, dropout_W=w_dropout_do, dropout_U=u_dropout_do,
# # implementation=1,
# # return_sequences=False if 3 == stack_layers else True,
# # go_backwards=False, stateful=False, unroll=False,
# # name='lstm_{}_w-drop_{}_u-drop_{}_layer_{}'.format(lstm_output_size, str(u_dropout_do),
# # str(w_dropout_do), str(3))))
# model.add(Dense(output_size, activation='sigmoid', name='sigmoid_output'))
# model.compile(optimizer='rmsprop', loss='binary_crossentropy')
# input_matrix = fh.join_paths(matrices_save_location, GLOBAL_VARS.MODEL_NAME)
# early_stopper = keras.callbacks.EarlyStopping(monitor='val_loss', min_delta=EARLY_STOPPER_MIN_DELTA,
# patience=EARLY_STOPPER_PATIENCE, verbose=1, mode='auto')
# metrics_callback = mh.MetricsCallback(input_matrix, classif_type, PARTS_LEVEL, NN_BATCH_SIZE, is_mlp=False)
# history, yvp, yvp_binary = pmh.fit_predict_generator(model, X_data, y_train, Xv_data, y_val, training_docs_list, val_docs_list, early_stopper, metrics_callback, NN_BATCH_SIZE, NN_MAX_EPOCHS, QUEUE_SIZE)
# validation_metrics = mh.get_sequential_metrics(y_val, yvp, yvp_binary)
# mh.display_sequential_metrics(validation_metrics)
# ###########
# useful to skip all the already tested models
if load_existing_results:
param_results_path = fh.link_paths(fh.join_paths(nn_parameter_search_location, GLOBAL_VARS.MODEL_NAME),
NN_PARAMETER_SEARCH_PREFIX.format(classif_type, classif_level, NN_BATCH_SIZE))
if fh.ensure_exists_path_location(param_results_path):
print('Loading Previous results in {}'.format(param_results_path))
param_results_dict = pickle.load(open(param_results_path, 'rb'))
else:
print('No Previous results exist in {}'.format(param_results_path))
for params in param_sampler:
start_time = time.time()
lstm_output_size = params['lstm_output_size']
w_dropout_do = params['w_dropout']
u_dropout_do = params['u_dropout']
stack_layers = params['stack_layers']
conv_size = params['conv_size']
conv_filter_length = params['conv_filter_length']
conv_max_pooling_length = params['conv_max_pooling_length']
GLOBAL_VARS.NN_MODEL_NAME = 'lstm_size_{}_w-drop_{}_u-drop_{}_stack_{}_conv_{}'.format(
lstm_output_size, w_dropout_do, u_dropout_do, stack_layers, str(conv_size))
if conv_size:
GLOBAL_VARS.NN_MODEL_NAME += '_conv-filter-length_{}_max-pooling-size_{}'.format(conv_filter_length,
conv_max_pooling_length)
if GLOBAL_VARS.NN_MODEL_NAME in param_results_dict.keys():
print("skipping: {}".format(GLOBAL_VARS.NN_MODEL_NAME))
continue
# creating the actual keras model
model = pmh.get_keras_rnn_model(NN_INPUT_NEURONS, NN_SEQUENCE_SIZE, NN_OUTPUT_NEURONS,
lstm_output_size, w_dropout_do, u_dropout_do, stack_layers, conv_size,
conv_filter_length, conv_max_pooling_length)
classifier_name, parameters = ch.get_sequential_classifier_information(model)
model_name = text_vectorizer+'/'+class_vectorizer+'/'+classifier_name
input_matrix = fh.join_paths(matrices_save_location, GLOBAL_VARS.MODEL_NAME)
early_stopper = keras.callbacks.EarlyStopping(monitor='val_loss', min_delta=EARLY_STOPPER_MIN_DELTA,
patience=EARLY_STOPPER_PATIENCE, verbose=1, mode='auto')
metrics_callback = mh.MetricsCallback(input_matrix, classif_type, PARTS_LEVEL, NN_BATCH_SIZE, is_mlp=False)
history, yvp, yvp_binary = pmh.fit_predict_generator(model, X_data, y_train, Xv_data, y_val, training_docs_list, val_docs_list, early_stopper, metrics_callback, NN_BATCH_SIZE, NN_MAX_EPOCHS, QUEUE_SIZE)
print('\nGenerating Validation Metrics')
validation_metrics = mh.get_sequential_metrics(y_val, yvp, yvp_binary)
mh.display_sequential_metrics(classifier_name, validation_metrics)
param_results_dict[GLOBAL_VARS.NN_MODEL_NAME] = dict()
# param_results_dict[GLOBAL_VARS.NN_MODEL_NAME]['best_validation_metrics'] = best_validation_metrics
param_results_dict[GLOBAL_VARS.NN_MODEL_NAME]['epochs'] = len(history.history['val_loss'])
param_results_dict[GLOBAL_VARS.NN_MODEL_NAME]['best_weights'] = metrics_callback.best_weights
param_results_dict[GLOBAL_VARS.NN_MODEL_NAME]['best_val_loss'] = metrics_callback.best_val_loss
param_results_dict[GLOBAL_VARS.NN_MODEL_NAME]['training_loss'] = metrics_callback.losses
param_results_dict[GLOBAL_VARS.NN_MODEL_NAME]['validation_loss'] = metrics_callback.val_losses
duration = time.time() - start_time
param_results_dict[GLOBAL_VARS.NN_MODEL_NAME]['duration'] = duration
ch.delete_variables(history, metrics_callback)
ch.save_results(classifier_name+'_LSTM', validation_metrics, parameters, model_name, classif_level, classif_type, dataset_location)
if save_results:
file = open(param_results_path, 'wb')
pickle.dump(param_results_dict, file)
def train_basic_LSTM(data_frame, text_vectorizer, classif_level, classif_type, dataset_location):
print('### LSTM - Training ###')
root_location = get_root_location('data/lstm_outcome/')
doc2vec_model_location = link_paths(join_paths(root_location, "doc2vec_model/vocab_model/"), "doc2vec_model")
model_location = link_paths(join_paths(root_location, "lstm_model"), "lstm_model")
sets_location = join_paths(root_location, "model_sets")
save_results = False
save_model = True
model_name = text_vectorizer+'/'+class_vectorizer+'/LSTM'
results = apply_df_vectorizer(data_frame, text_vectorizer, class_vectorizer, model_name)
X_train, X_val, y_train, y_val, classes, n_classes, vocab_processor, len_vocabulary = results
save_sets(sets_location, X_train, None, X_val, y_train, None, y_val, [classes, n_classes, vocab_processor, len_vocabulary])
# val_path problem
training_docs_list = X_train['patent_id']
val_docs_list = X_val['patent_id']
sequence_size, embedding_size = 1, 150
X_data, Xv_data, _ = ch.get_df_data(2, training_docs_list, val_docs_list, None, sequence_size, embedding_size, doc2vec_model_location)
# sequence size is the (, number, ) in the tuple data
print(X_data.shape)
input_size, output_size = X_data.shape[2], n_classes
parameters = {
"estimator__epochs": 200,
"estimator__batch_size": 64,
"estimator__optimizer_1": 'Adam',
"estimator__optimizer_2": 'Adam',
"estimator__optimizer_3": 'Adam',
"estimator__optimizer_4": 'Adam',
"estimator__optimizer_5": 'Adam',
"estimator__optimizer_6": 'Adam',
"estimator__init_mode_1": 'uniform',
"estimator__init_mode_2": 'uniform',
"estimator__init_mode_3": 'uniform',
"estimator__init_mode_4": 'uniform',
"estimator__init_mode_5": 'uniform',
"estimator__init_mode_6": 'uniform',
"estimator__activation_1": 'softmax',
"estimator__activation_2": 'softmax',
"estimator__activation_3": 'softmax',
"estimator__activation_4": 'softmax',
"estimator__activation_5": 'softmax',
"estimator__activation_6": 'softmax',
"estimator__dropout_rate_1": .0,
"estimator__dropout_rate_2": .0,
"estimator__dropout_rate_3": .0,
"estimator__dropout_rate_4": .0,
"estimator__weight_constraint_1": 1,
"estimator__weight_constraint_2": 1,
"estimator__weight_constraint_3": 1,
"estimator__weight_constraint_4": 1,
"estimator__weight_constraint_5": 1,
"estimator__weight_constraint_6": 1,
"estimator__neurons_1": n_classes*20,
"estimator__neurons_2": n_classes*5,
"estimator__neurons_3": n_classes*2,
"estimator__neurons_4": n_classes*1.5,
"estimator__filters": 16,
"estimator__filters_2": 16,
"estimator__filters_3": 16,
"estimator__kernel_size_1": 8,
"estimator__kernel_size_2": 8, "estimator__kernel_size_3": 8,
"estimator__strides_1": 8,
"estimator__strides_2": 8,
"estimator__strides_3": 8,
"estimator__activation_lstm_1": 'tanh',
"estimator__activation_lstm_2": 'tanh',
"estimator__activation_lstm_3": 'tanh',
"estimator__recurrent_activation_1": 'hard_sigmoid',
"estimator__recurrent_activation_2": 'hard_sigmoid',
"estimator__recurrent_activation_3": 'hard_sigmoid',
"estimator__w_dropout_do_1": .2,
"estimator__w_dropout_do_2": .2,
"estimator__w_dropout_do_3": .2,
"estimator__u_dropout_do_1": .2,
"estimator__u_dropout_do_2": .2,
"estimator__u_dropout_do_3": .2,
"estimator__backwards_1": False,
"estimator__backwards_2": False,
"estimator__backwards_3": False,
"estimator__unroll_1": False,
"estimator__unroll_2": False,
"estimator__unroll_3": False
}
model = get_lstm(optimizer,
init_mode_1, activation_1,
init_mode_2, activation_2,
init_mode_3, activation_3,
init_mode_4, activation_4,
init_mode_5, activation_5,
init_mode_6, activation_6,
weight_constraint_1,
weight_constraint_2,
weight_constraint_3,
weight_constraint_4,
weight_constraint_5,
weight_constraint_6,
dropout_rate_1,
dropout_rate_2,
dropout_rate_3,
dropout_rate_4,
neurons_1, neurons_2, neurons_3,
filters_1, filters_2, filters_3,
kernel_size_1, kernel_size_2, kernel_size_3,
strides_1, strides_2, strides_3,
activation_lstm_1, activation_lstm_2, activation_lstm_3,
recurrent_activation_1, recurrent_activation_2, recurrent_activation_3,
w_dropout_do_1, w_dropout_do_2, w_dropout_do_3,
u_dropout_do_1, u_dropout_do_2, u_dropout_do_3,
backwards_1, backwards_2, backwards_3,
unroll_1, unroll_2, unroll_3,
lstm_output_size_1, lstm_output_size_2, lstm_output_size_3,
input_size) # input size
# check the X_data shape and vocabulary size
# check the X_data shape and vocabulary size
if save_model:
model.save(model_location)
min_delta, patience = 0.00001, 15
early_stopper = keras.callbacks.EarlyStopping(monitor='val_loss', min_delta=min_delta, patience=patience, verbose=1, mode='auto')
metrics_callback = MetricsCNNCallback(Xv_data, y_val, patience)
metrics, val_predictions, val_metrics = run_lstm(model,
X_data, y_train, Xt_data, y_test, Xv_data, y_val,
batch_size, [early_stopper, metrics_callback], queue_size,
training_docs_list, val_docs_list)
binary_val_predictions = mh.get_binary_0_5(val_predictions)
print('\nGenerating Validation Metrics')
validation_metrics = mh.get_sequential_metrics(y_val, val_predictions, binary_val_predictions)
mh.display_sequential_metrics(validation_metrics)
if save_results:
classifier_name, parameters = ch.get_sequential_classifier_information(model)
model_name = text_vectorizer+'/'+class_vectorizer+'/'+classifier_name
ch.save_results(classifier_name+'_LSTM', validation_metrics, parameters, model_name, classif_level, classif_type, dataset_location)
print('end training step')