def train(self):
""" Start training """
# 1: build a list of image filenames
self.build_image_filenames_list()
# 2: use list information to init our numpy variables
self.init_np_variables()
# 3: Add images to our Tensorflow dataset
self.add_tf_dataset(self.list_cow_files, 0)
self.add_tf_dataset(self.list_noncow_files, 1)
# 4: Process TF dataset
self.process_tf_dataset()
# 5: Setup image preprocessing
self.setup_image_preprocessing()
# 6: Setup network structure
self.setup_nn_network()
# 7: Train our deep neural network
tf_model = DNN(self.tf_network, tensorboard_verbose=3,
checkpoint_path='model_cows.tfl.ckpt')
tf_model.fit(self.tf_x, self.tf_y, n_epoch=100, shuffle=True,
validation_set=(self.tf_x_test, self.tf_y_test),
show_metric=True, batch_size=96,
snapshot_epoch=True,
run_id='model_cows')
# 8: Save model
tf_model.save('model_cows.tflearn')
def train(
self,
X_train,
Y_train,
X_val,
Y_val
):
with tf.Graph().as_default():
print("Building Model...........")
network = build_CNN()
model = DNN(
network,
tensorboard_dir="path_to_logs",
tensorboard_verbose=0,
checkpoint_path="path_to_checkpoints",
max_checkpoints=1
)
if self.is_training:
# Training phase
print("start training...")
print(" - emotions = {}".format(7))
print(" - optimizer = '{}'".format(self.optimizer))
print(" - learning_rate = {}".format(0.016))
print(" - learning_rate_decay = {}".format(self.learning_rate_decay))
print(" - otimizer_param ({}) = {}".format(self.optimizer, self.optimizer_param))
print(" - Dropout = {}".format(self.dropout))
print(" - epochs = {}".format(self.epochs))
start_time = time.time()
model.fit(
{'input': X_train.reshape(-1, 48, 48, 1)},
{'output': Y_train},
validation_set=(
{'input': X_val.reshape(-1, 48, 48, 1)},
{'output': Y_val},
),
batch_size=128,
n_epoch=10,
show_metric=True,
snapshot_step=100
)
training_time = time.time() - start_time
print("training time = {0:.1f} sec".format(training_time))
print("saving model...")
model.save("saved_model.bin")
def use_tflearn(x_train, y_train, x_test, y_test):
net = input_data(shape=[None, x_train.shape[1]], name='input')
net = fully_connected(net, 24, activation='sigmoid', bias_init='normal')
net = fully_connected(net, 16, activation='sigmoid', bias_init='normal')
net = fully_connected(net, 12, activation='sigmoid', bias_init='normal')
net = fully_connected(net, 8, activation='sigmoid', bias_init='normal')
net = regression(net)
model = DNN(net,
tensorboard_dir=TENSORBOARD_DIR.as_posix(),
tensorboard_verbose=3,
best_checkpoint_path=CHECKPOINT_PATH.as_posix())
model.fit(x_train, y_train,
validation_set=(x_test, y_test),
n_epoch=100,
batch_size=10,
show_metric=True,
run_id='DNN-4f')
model.save(MODEL_FILE.as_posix())
return model
"""
Source : https://towardsdatascience.com/tflearn-soving-xor-with-a-2x2x1-feed-forward-neural-network-6c07d88689ed
"""
from tflearn import DNN
from tflearn.layers.core import input_data, dropout, fully_connected
from tflearn.layers.estimator import regression
#Training examples
X = [[0,0], [0,1], [1,0], [1,1]]
Y = [[0], [1], [1], [0]]
input_layer = input_data(shape=[None, 2]) #input layer of size 2
hidden_layer = fully_connected(input_layer , 2, activation='tanh') #hidden layer of size 2
output_layer = fully_connected(hidden_layer, 1, activation='tanh') #output layer of size 1
#use Stohastic Gradient Descent and Binary Crossentropy as loss function
regression = regression(output_layer , optimizer='sgd', loss='binary_crossentropy', learning_rate=5)
model = DNN(regression)
#fit the model
model.fit(X, Y, n_epoch=5000, show_metric=True);
#predict all examples
print ('Expected: ', [i[0] > 0 for i in Y])
print ('Predicted: ', [i[0] > 0 for i in model.predict(X)])
model.get_weights(hidden_layer.W)
model.get_weights(output_layer.W)
model.save("tflearn-xor")
def train(optimizer=HYPERPARAMS.optimizer,
optimizer_param=HYPERPARAMS.optimizer_param,
learning_rate=HYPERPARAMS.learning_rate,
keep_prob=HYPERPARAMS.keep_prob,
learning_rate_decay=HYPERPARAMS.learning_rate_decay,
decay_step=HYPERPARAMS.decay_step,
train_model=True):
print "loading dataset " + DATASET.name + "..."
if train_model:
data, validation = load_data(validation=True)
else:
data, validation, test = load_data(validation=True, test=True)
with tf.Graph().as_default():
print "building model..."
network = build_model(optimizer, optimizer_param, learning_rate,
keep_prob, learning_rate_decay, decay_step)
model = DNN(network,
tensorboard_dir=TRAINING.logs_dir,
tensorboard_verbose=0,
checkpoint_path=TRAINING.checkpoint_dir,
max_checkpoints=TRAINING.max_checkpoints)
#tflearn.config.init_graph(seed=None, log_device=False, num_cores=6)
if train_model:
# Training phase
print "start training..."
print " - emotions = {}".format(NETWORK.output_size)
print " - optimizer = '{}'".format(optimizer)
print " - learning_rate = {}".format(learning_rate)
print " - learning_rate_decay = {}".format(learning_rate_decay)
print " - otimizer_param ({}) = {}".format(
'beta1' if optimizer == 'adam' else 'momentum',
optimizer_param)
print " - keep_prob = {}".format(keep_prob)
print " - epochs = {}".format(TRAINING.epochs)
print " - use landmarks = {}".format(NETWORK.use_landmarks)
print " - use hog + landmarks = {}".format(
NETWORK.use_hog_and_landmarks)
print " - use hog sliding window + landmarks = {}".format(
NETWORK.use_hog_sliding_window_and_landmarks)
print " - use batchnorm after conv = {}".format(
NETWORK.use_batchnorm_after_conv_layers)
print " - use batchnorm after fc = {}".format(
NETWORK.use_batchnorm_after_fully_connected_layers)
start_time = time.time()
if NETWORK.use_landmarks:
model.fit([data['X'], data['X2']],
data['Y'],
validation_set=([validation['X'],
validation['X2']], validation['Y']),
snapshot_step=TRAINING.snapshot_step,
show_metric=TRAINING.vizualize,
batch_size=TRAINING.batch_size,
n_epoch=TRAINING.epochs)
else:
model.fit(data['X'],
data['Y'],
validation_set=(validation['X'], validation['Y']),
snapshot_step=TRAINING.snapshot_step,
show_metric=TRAINING.vizualize,
batch_size=TRAINING.batch_size,
n_epoch=TRAINING.epochs)
validation['X2'] = None
training_time = time.time() - start_time
print "training time = {0:.1f} sec".format(training_time)
if TRAINING.save_model:
print "saving model..."
model.save(TRAINING.save_model_path)
if not(os.path.isfile(TRAINING.save_model_path)) and \
os.path.isfile(TRAINING.save_model_path + ".meta"):
os.rename(TRAINING.save_model_path + ".meta",
TRAINING.save_model_path)
print "evaluating..."
validation_accuracy = evaluate(model, validation['X'],
validation['X2'], validation['Y'])
print " - validation accuracy = {0:.1f}".format(
validation_accuracy * 100)
return validation_accuracy
else:
# Testing phase : load saved model and evaluate on test dataset
print "start evaluation..."
print "loading pretrained model..."
if os.path.isfile(TRAINING.save_model_path):
model.load(TRAINING.save_model_path)
else:
print "Error: file '{}' not found".format(
TRAINING.save_model_path)
exit()
if not NETWORK.use_landmarks:
validation['X2'] = None
test['X2'] = None
print "--"
print "Validation samples: {}".format(len(validation['Y']))
print "Test samples: {}".format(len(test['Y']))
print "--"
print "evaluating..."
start_time = time.time()
validation_accuracy = evaluate(model, validation['X'],
validation['X2'], validation['Y'])
print " - validation accuracy = {0:.1f}".format(
validation_accuracy * 100)
test_accuracy = evaluate(model, test['X'], test['X2'], test['Y'])
print " - test accuracy = {0:.1f}".format(test_accuracy * 100)
print " - evalution time = {0:.1f} sec".format(time.time() -
start_time)
return test_accuracy
class DNNBackend(BaseBackend):
def create_model(self):
"""
Creates DNN model that is based on built algorithm.
Needed algorithm is builded with self.build_algorithm call.
"""
self.log_named("model creation started")
if self.algorithm is not None:
self.model = DNN(self.algorithm,
checkpoint_path=self.checkpoints_dir_path,
max_checkpoints=1,
tensorboard_verbose=3,
tensorboard_dir=self.learn_logs_dir_path)
self.log_named("model creation finished")
else:
self.log_named_warning(
"model was not created, because algorithm is None!")
def save_model(self):
"""
Saves created DNN model to a file.
Path to is got from self.model_file_path property.
"""
if self.model is not None:
self.model.save(self.model_file_path)
self.log_named("model saved")
else:
self.log_named_warning(
"model file was not saved, because model is None!")
def load_model(self):
"""
Loads saved DNN model from a file.
Path to is got from self.model_file_path property.
"""
if self.model is not None:
if os.path.exists(self.model_file_dir_path) and len(
os.listdir(self.model_file_dir_path)):
self.model.load(self.model_file_path)
self.log_named("model loaded")
else:
self.log_named_warning("model file doesn't exist!")
else:
self.log_named_warning("model is None!")
def restore_model_learning(self):
"""
Restores model learning from the last checkpoint if such exists.
"""
if self.model is not None:
if os.path.exists(self.checkpoints_dir_path):
with open(os.path.join(self.checkpoints_dir_path,
'checkpoint')) as checkpoint_file:
self.model.load(
checkpoint_file.readline().split(': ')[-1][1:-2])
self.learn_model()
self.save_model()
else:
self.log_named_warning("checkpoints directory doesn't exist!")
else:
self.log_named_warning(
"can't restore model learning process, because model is None!")
class Bot:
def __init__(self):
self.words = []
self.labels = []
self.docs_x = []
self.docs_y = []
self.stemmer = LancasterStemmer()
self.data = []
self.training = []
self.output = []
self.out_empty=[]
self.model=[]
self.count=-1
self.say=""
self.Network=Network()
def read(self):
with open("src/models/intents.json") as f:
self.data=load(f)
def dump(self):
with open("src/models/data.pickle", "wb") as f:
dump((self.words, self.labels, self.training, self.output), f)
def stem(self):
for intent in self.data["intents"]:
for pattern in intent["patterns"]:
wrds = word_tokenize(pattern)
self.words.extend(wrds)
self.docs_x.append(wrds)
self.docs_y.append(intent["tag"])
if intent["tag"] not in self.labels:
self.labels.append(intent["tag"])
self.words = [self.stemmer.stem(w.lower()) for w in self.words if w != "?"]
self.words = sorted(list(set(self.words)))
self.labels = sorted(self.labels)
def modelsetup(self):
self.out_empty = [0 for _ in range(len(self.labels))]
for x, doc in enumerate(self.docs_x):
bag = []
wrds = [self.stemmer.stem(w.lower()) for w in doc]
for w in self.words:
if w in wrds:
bag.append(1)
else:
bag.append(0)
output_row = self.out_empty[:]
output_row[self.labels.index(self.docs_y[x])] = 1
self.training.append(bag)
self.output.append(output_row)
self.training = array(self.training)
self.output = array(self.output)
self.dump()
def setup(self):
ops.reset_default_graph()
net = input_data(shape=[None, len(self.training[0])])
net = fully_connected(net, 10)
net = fully_connected(net, 10)
net = fully_connected(net, len(self.output[0]), activation="softmax")
net = regression(net)
self.model = DNN(net)
if exists("src/models/model.tflearn.index"):
self.model.load("src/models/model.tflearn")
else:
self.model.fit(self.training, self.output, n_epoch=1000, batch_size=8, show_metric=True)
self.model.save("src/models/model.tflearn")
def indexWord(self,x,word):
x=x.split(" ")
ch=""
for i in x:
if i.find(word)!=-1:
ch=i
return ch
def bag_of_words(self,s, words):
bag = [0 for _ in range(len(words))]
translate=[]
s_words = word_tokenize(s)
s_words = [self.stemmer.stem(word.lower()) for word in s_words]
for se in s_words:
for i, w in enumerate(words):
if w == se:
bag[i] = 1
if se not in words and se not in translate:
translate.append(se)
return array(bag),translate
def chat(self,x,ui):
try:
self.count+=1
predinp,translate=self.bag_of_words(x, self.words)
if translate:
translate=self.indexWord(str(x),translate[0])
print(translate)
results = self.model.predict([predinp])
results_index = argmax(results)
tag = self.labels[results_index]
except Exception as e:
print(e)
try:
if results[0][results_index] > 0.4:
for tg in self.data["intents"]:
if tg['tag'] == tag:
responses = tg['responses']
self.say=choice(responses)
if self.say=="Looking up":
self.say=self.Network.Connect(translate.upper())
ui.textEdit.setText(self.say)
else:
ui.textEdit.setText(self.say)
else:
self.say="Sorry i can't understand i am still learning try again."
ui.textEdit.setText(self.say)
except Exception as e:
print(e)
