def validateModel(self,x,y):
print("\n\nValidating Model")
print("\nLoaded pre-trained model from disc")
self.classes = sorted(getClasses())
#validation
val_loss,val_acc = self.model.evaluate(x,y)
ta_val = PrettyTable(['Loss (%)', 'Accuracy (%)'])
ta_val.add_row([val_loss*100,val_acc*100])
#validation table
print("\n\n{}".format(ta_val))
#prediction
predict = self.model.predict(x)
print("\n\nPrediction:\n")
t_tot = PrettyTable(['Sl.No.', 'Argmax Value','Expected Value','Predicted Tag','Expected Tag','Result'])
for i in range(len(predict)):
argMax = np.argmax(predict[i])
tag = self.classes[argMax]
result = "Correct" if y[i] == argMax else "Wrong"
t_tot.add_row([i+1,argMax,y[i],tag,y[i],result])
#Total table
print(t_tot)
return predict,val_loss,val_acc
def trainModel(self,x,y,epochs = 50,test_size = 0.2,batch_size = 10):
disp = PrettyTable(['Training TChatBot model.....'])
self.classes = sorted(getClasses())
inp_shape = len(x[0])
x_train ,x_test , y_train, y_test = model_selection.train_test_split(x,y, test_size = test_size)
self.model = tf.keras.models.Sequential()
self.model.add(tf.keras.layers.Flatten(input_shape=(inp_shape,)))
self.model.add(tf.keras.layers.Dense(300,activation = "relu"))
self.model.add(tf.keras.layers.Dense(200,activation = "relu"))
self.model.add(tf.keras.layers.Dense(40,activation = "relu"))
# model.add(tf.keras.layers.Dense(10,activation = "relu"))
self.model.add(tf.keras.layers.Dense(len(self.classes),activation = "sigmoid"))
self.model.compile(optimizer = "adam",
loss = 'sparse_categorical_crossentropy',
metrics=['accuracy'])
disp.add_row(['Laying the pipeline for the model'])
print("\n{}\n".format(disp))
self.model.summary()
print("\n\n")
time.sleep(1)
self.model.fit(x_train ,y_train , epochs =epochs,batch_size = batch_size)
self.model.save(saved_model_path)
print("\n\n-----+-----+-----+-----+-----+-----+-----+------+------+-----+------+------")
print(" Saving trained Model......")
print("-----+-----+-----+-----+-----+-----+-----+------+------+-----+------+------")
print("Model saved in disc as \'model.h5\' file in path: {}".format(saved_model_path))
print("-----+-----+-----+-----+-----+-----+-----+------+------+-----+------+------\n")
def trainModel(self,x,y,epochs = 50,test_size = 0.2,batch_size = 10):
disp = PrettyTable(['Training TChatBot model.....'])
self.classes = sorted(getClasses())
inp_shape = len(x[0])
x_train ,x_test , y_train, y_test = model_selection.train_test_split(x,y, test_size = test_size)
"""DNN Architecture"""
self.model = Sequential()
self.model.add(Flatten(input_shape=(inp_shape,)))
self.model.add(Dense(300,activation = "relu"))
self.model.add(Dense(200,activation = "relu"))
self.model.add(Dense(40,activation = "relu"))
self.model.add(Dense(len(self.classes),activation = "sigmoid"))
"""CNN Architecture"""
# self.model = Sequential()
# self.model.add(Conv1D(filters=64, kernel_size=1, activation='relu', input_shape=inp_shape))
# self.model.add(Conv1D(filters=32, kernel_size=1, activation='relu'))
# self.model.add(MaxPooling1D(pool_size=1))
# self.model.add(Flatten())
# self.model.add(Dense(100, activation='relu'))
# self.model.add(Dense(len(self.classes), activation='sigmoid'))
self.model.compile(optimizer = "adam",
loss = 'sparse_categorical_crossentropy',
metrics=['accuracy'])
disp.add_row(['Laying the pipeline for the model'])
print("\n{}\n".format(disp))
self.model.summary()
print("\n\n")
time.sleep(1)
self.model.fit(x_train ,y_train , epochs =epochs,batch_size = batch_size)
path_to_model = saved_model_path / model_name
path = os.path.join(path_to_model,model_version)
try:
os.makedirs(path, exist_ok = True)
tf.saved_model.save(self.model,path)
print("\n\n-----+-----+-----+-----+-----+-----+-----+------+------+-----+------+------")
print(" Saving trained Model version {}......".format(model_version))
print("-----+-----+-----+-----+-----+-----+-----+------+------+-----+------+------")
print("Model saved in disc as \'saved_model.pb\' file in path: {}".format("saved_model/{}".format(model_name)+"/"+model_version))
print("-----+-----+-----+-----+-----+-----+-----+------+------+-----+------+------\n")
except OSError as error:
print("Path already exists")
def predictModel(self,x):
print("Loaded pre-trained model from disc")
self.classes = getClasses()
tags = []
tag_indices = []
#prediction
predict = self.model.predict(x)
print("Prediction:\n")
t_pred = PrettyTable(['Sl.No.', 'Argmax Value','Predicted Tag'])
for i in range(len(predict)):
argMax = np.argmax(predict[i])
tag_indices.append(argMax)
tag = self.classes[argMax]
tags.append(tag)
t_pred.add_row([i+1,argMax,tag])
print(t_pred)
return predict,tags,tag_indices
def TrainChatBot(epochs=50, test_size=0.1, batch_size=8):
model = ChatBotModel()
#load JSON data
data = load_JSON(json_path)
# #Load stemmed data from pickle from the training dataset
words, labels, training, output = ProcessData(data, train=True)
time.sleep(2)
#load classes (tags)
tags = getClasses()
ta_class = PrettyTable(['Classes'])
for i in range(len(tags)):
ta_class.add_row([tags[i]])
print("\n\n{}\n\n".format(ta_class))
time.sleep(2)
#Train model
model.trainModel(training, output, epochs, test_size, batch_size)
def chat(self,words,data):
time.sleep(1)
clearScreen()
print("------+------+-----+------+-----+-----+-----+------+------")
print(" TChatBot Session Initializing......")
print("------+------+-----+------+-----+-----+-----+------+------\n")
time.sleep(2)
print("TChatBot Session started at : {} ".format(time.ctime()))
print("Type \'quit\' to end chat session")
print("\n------+------+-----+------+-----+-----+-----+------+------")
time.sleep(1)
print("\nTChatBot (Bot): Hey! I'm TChatBot :)")
classes = getClasses()
sort_classes = sorted(classes)
while True:
time.sleep(1)
inp = input("You: ")
if(inp.lower()=='quit'):
print("\n\n------+------+-----+------+-----+-----+-----+------+------")
print("TChatBot (Bot): Comeback again next time ;)")
print("TChatBot session expired at : {} ".format(time.ctime()))
print("------+------+-----+------+-----+-----+-----+------+------\n")
break
chatOut = self.model.predict([bagOfWords(inp,words)])[0]
chatOut_index = np.argmax(chatOut)
tag = sort_classes[chatOut_index]
intents = data["intents"]
# confidence = chatOut[chatOut_index]
#Loop to randomly choose a reply
for i in range(len(intents)):
if intents[i]["tag"] == tag:
responses = intents[i]["responses"]
reply = np.random.choice(responses, 1, replace=False)[0]
print("\nTChatBot (Bot): {}".format(reply))