def predict_fn(input_data, model):
print('Inferring sentiment of input data.')
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
if model.word_dict is None:
raise Exception('Model has not been loaded properly, no word_dict.')
# Process input_data so that it is ready to be sent to our model.
data_X, data_len = convert_and_pad(model.word_dict, review_to_words(input_data))
# Using data_X and data_len we construct an appropriate input tensor. Remember
# that our model expects input data of the form 'len, review[500]'.
data_pack = np.hstack((data_len, data_X))
data_pack = data_pack.reshape(1, -1)
data = torch.from_numpy(data_pack)
data = data.to(device)
# Make sure to put the model into evaluation mode
model.eval()
# Compute the result of applying the model to the input data.
with torch.no_grad():
output = model(data)
result = np.round(output.numpy())
return result
def predict_fn(input_data, model):
print('Inferring sentiment of input data.')
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
if model.word_dict is None:
raise Exception('Model has not been loaded properly, no word_dict.')
# TODO: Process input_data so that it is ready to be sent to our model.
# You should produce two variables:
# data_X - A sequence of length 500 which represents the converted review
# data_len - The length of the review
data_X, data_len = convert_and_pad(model.word_dict, review_to_words(input_data))
# Using data_X and data_len we construct an appropriate input tensor. Remember
# that our model expects input data of the form 'len, review[500]'.
data_pack = np.hstack((data_len, data_X))
data_pack = data_pack.reshape(1, -1)
data = torch.from_numpy(data_pack)
data = data.to(device)
# Make sure to put the model into evaluation mode
model.eval()
# TODO: Compute the result of applying the model to the input data. The variable `result` should
# be a numpy array which contains a single integer which is either 1 or 0
with torch.no_grad():
output = model.forward(data)
result = int(np.round(output.numpy()))
return result
def predict_fn(input_data, model):
print('Inferring sentiment of input data.')
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
if model.word_dict is None:
raise Exception('Model has not been loaded properly, no word_dict.')
# Process input_data so that it is ready to be sent to our model.
# It produces two variables:
# data_X - A sequence of length 500 which represents the converted review
# data_len - The length of the review
data_x, data_len = convert_and_pad(model.word_dict,
review_to_words(input_data))
# Using data_X and data_len we construct an appropriate input tensor. Remember
# that our model expects input data of the form 'len, review[500]'.
data_pack = np.hstack((data_len, data_X))
data_pack = data_pack.reshape(1, -1)
data = torch.from_numpy(data_pack)
data = data.to(device)
# Put the model into evaluation mode
model.eval()
# Compute the result of applying the model to the input data.
result = np.array(round(model(data).item()))
return result
def predict_fn(input_data, model):
print('Inferring sentiment of input data.')
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
if model.word_dict is None:
raise Exception('Model has not been loaded properly, no word_dict.')
words = review_to_words(input_data)
data_X, data_len = convert_and_pad(model.word_dict, words)
# Using data_X and data_len we construct an appropriate input tensor. Remember
# that our model expects input data of the form 'len, review[500]'.
data_pack = np.hstack((data_len, data_X))
data_pack = data_pack.reshape(1, -1)
data = torch.from_numpy(data_pack)
data = data.to(device)
model.eval()
with torch.no_grad():
output = model.forward(data)
result = np.round(output.numpy()).astype(int)
return result
def predict_fn(input_data, model):
print('Inferring sentiment of input data.')
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
if model.word_dict is None:
raise Exception('Model has not been loaded properly, no word_dict.')
# TODO: Process input_data so that it is ready to be sent to our model.
# You should produce two variables:
# data_X - A sequence of length 500 which represents the converted review
# data_len - The length of the review
data_X_to_words = review_to_words(input_data)
# See function parameters for convert_and_pad from utils.py.
# Function has 2 outputs. 1) Review itself, 2) Length of the review, without padding. We can extract both values using Python indexing.
data_X = convert_and_pad(model.word_dict, data_X_to_words)[0]
data_len = convert_and_pad(model.word_dict, data_X_to_words)[1]
# Using data_X and data_len we construct an appropriate input tensor. Remember
# that our model expects input data of the form 'len, review[500]'.
data_pack = np.hstack((data_len, data_X))
data_pack = data_pack.reshape(1, -1)
data = torch.from_numpy(data_pack)
data = data.to(device)
# Make sure to put the model into evaluation mode
model.eval()
# TODO: Compute the result of applying the model to the input data. The variable `result` should
# be a numpy array which contains a single integer which is either 1 or 0
# https://discuss.pytorch.org/t/model-eval-vs-with-torch-no-grad/19615/2
# At the above link, you can find the discussion in a forum on why we should use torch.no_grad() in evaluation mode. HINT: It is much
# faster and ligher on memory while making predictions.
with torch.no_grad():
output = model.forward(data)
result = np.round(output.numpy())
return result
def convert_and_pad_data(word_dict, data, pad=500):
result = []
lengths = []
for sentence in data:
converted, lengths = convert_and_pad(word_dict, sentence, pad)
result.append(converted)
lengths.append(lengths)
return np.array(result), np.array(lengths)
def predict_fn(input_data, model):
print('Inferring sentiment of input data.')
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
if model.word_dict is None:
raise Exception('Model has not been loaded properly, no word_dict.')
# TODO: Process input_data so that it is ready to be sent to our model.
# You should produce two variables:
# data_X - A sequence of length 500 which represents the converted review
# data_len - The length of the review
test_X = review_to_words(
input_data) # converting to words and applying filters
"""
We are only sending one review at a time, so we won't need to do it as we did during training
data_X = []
data_len = []
for sentence in data:
converted, leng = convert_and_pad(word_dict, sentence, pad)
result.append(converted)
lengths.append(leng)
return np.array(result), np.array(lengths)
"""
data_X, data_len = convert_and_pad(
model.word_dict, test_X) # converting to a 500 bow features
# Using data_X and data_len we construct an appropriate input tensor. Remember
# that our model expects input data of the form 'len, review[500]'.
data_pack = np.hstack((data_len, data_X))
data_pack = data_pack.reshape(1, -1)
data = torch.from_numpy(data_pack)
data = data.to(device)
# Make sure to put the model into evaluation mode
model.eval()
# TODO: Compute the result of applying the model to the input data. The variable `result` should
# be a numpy array which contains a single integer which is either 1 or 0
with torch.no_grad():
output = model.forward(
data) # making forward pass through the trained network
output = output.to(
'cpu') # switching to CPU to prevent deployment issue
result = np.round(output.numpy())
result = int(result)
return result
def predict_fn(input_data, model):
"""Function to infer the sentiment of provided input data and
generate a prediction from the trained model.
Args:
input_data (str): unprocessed input data of IMDB reviews
model (LSTMClassifier): type of input for deserialization
Returns:
Exception: thrown if content_type not equal to 'text/plain'
"""
print('Inferring sentiment of input data.')
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
if model.word_dict is None:
raise Exception('Model has not been loaded properly, no word_dict.')
# DONE: Process input_data so that it is ready to be sent to our model
# You should produce two variables:
# data_X - A sequence of length 500 which represents the converted review
# data_len - The length of the review
data_X, data_len = convert_and_pad(model.word_dict,
review_to_words(input_data))
# Using data_X and data_len we construct an appropriate input tensor
# Remember that our model expects input data of the form 'len, review[500]'
data_pack = np.hstack((data_len, data_X))
data_pack = data_pack.reshape(1, -1)
data = torch.from_numpy(data_pack)
data = data.to(device)
# Make sure to put the model into evaluation mode
model.eval()
# DONE: Compute the result of applying the model to the input data
# The variable `result` should be a numpy array which contains a
# single integer which is either 1 or 0
with torch.no_grad():
output = model.forward(data)
result = np.round(output.numpy())
return result
def predict_fn(input_data, model):
print('Inferring sentiment of input data.')
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
if model.word_dict is None:
raise Exception('Model has not been loaded properly, no word_dict.')
# TODO: Process input_data so that it is ready to be sent to our model.
# You should produce two variables:
# data_X - A sequence of length 500 which represents the converted review
# data_len - The length of the review
print("Input data = {}".format(input_data))
#First - convert to words
sentence = review_to_words(input_data)
print("Converted to words = {}".format(sentence))
#Second - convert to int and pad to 500
data_X, data_len = convert_and_pad(model.word_dict, sentence, 500)
print("Converted to int array = {}".format(data_X))
print("Length = {}".format(data_len))
# Using data_X and data_len we construct an appropriate input tensor. Remember
# that our model expects input data of the form 'len, review[500]'.
data_pack = np.hstack((data_len, data_X))
data_pack = data_pack.reshape(1, -1)
data = torch.from_numpy(data_pack)
data = data.to(device)
print("Data formatted in a tensor = {}".format(data))
# Make sure to put the model into evaluation mode
model.eval()
# TODO: Compute the result of applying the model to the input data. The variable `result` should
# be a numpy array which contains a single integer which is either 1 or 0
output = model(data)
print("Model output = {}".format(output))
# convert to int and put in numpy array
result = output.detach().numpy().round().astype(int)
print("Result = {}".format(result))
return result
def predict_fn(input_data, model):
print('Inferring sentiment of input data.')
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
if model.word_dict is None:
raise Exception('Model has not been loaded properly, no word_dict.')
# TODO: Process input_data so that it is ready to be sent to our model.
# You should produce two variables:
# data_X - A sequence of length 500 which represents the converted review
# data_len - The length of the review
data_X, data_len = convert_and_pad(model.word_dict,
review_to_words(input_data))
data_X, data_len = np.array(data_X), np.array(data_len)
# Using data_X and data_len we construct an appropriate input tensor. Remember
# that our model expects input data of the form 'len, review[500]'.
data_pack = np.hstack((data_len, data_X))
data_pack = data_pack.reshape(1, -1)
data = torch.from_numpy(data_pack)
data = data.to(device)
# Make sure to put the model into evaluation mode
model.eval()
# TODO: Compute the result of applying the model to the input data. The variable `result` should
# be a numpy array which contains a single integer which is either 1 or 0
# Reference: https://knowledge.udacity.com/questions/23616
# Reference: https://study-hall.udacity.com/rooms/community:nd101:633452-cohort-2455-project-2262/community:thread-271916691-456216?contextType=room
# Reference: https://study-hall.udacity.com/rooms/community:nd101:633452-cohort-2455-project-2262/community:thread-u26834124-410140?contextType=room
with torch.no_grad():
# Take a forward path
output = model.forward(data)
# TO DO: Study why this is required? It is non-intuitive.
# Move the result to cpu (this is a must if GPU was used)
output = output.to('cpu')
# TO DO: Study why this is required? It is non-intuitive.
# Make the result an int (0 or 1)
result = np.round(output.numpy())
return result
def predict_fn(input_data, model):
print('Inferring sentiment of input data.')
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
if model.word_dict is None:
raise Exception('Model has not been loaded properly, no word_dict.')
# TODO: Process input_data so that it is ready to be sent to our model.
# You should produce two variables:
# data_X - A sequence of length 500 which represents the converted review
# data_len - The length of the review
converted, length = convert_and_pad(model.word_dict,
review_to_words(input_data))
data_X = converted
data_len = length
# Using data_X and data_len we construct an appropriate input tensor. Remember
# that our model expects input data of the form 'len, review[500]'.
data_pack = np.hstack((data_len, data_X))
data_pack = data_pack.reshape(1, -1)
data = torch.from_numpy(data_pack)
data = data.to(device)
# Make sure to put the model into evaluation mode
model.eval()
# TODO: Compute the result of applying the model to the input data. The variable `result` should
# be a numpy array which contains a single integer which is either 1 or 0
# AdVo: source
# https://sagemaker.readthedocs.io/en/stable/frameworks/pytorch/using_pytorch.html#train-a-model-with-pytorch
with torch.no_grad():
result = model(data)
# Convert the result from Tensor to numpy
result = result.numpy()
# Round the result and convert to int
result = np.array(int(np.around(result)))
return result
def predict_fn(input_data, model):
print('Inferring sentiment of input data.')
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
if model.word_dict is None:
raise Exception('Model has not been loaded properly, no word_dict.')
# TODO: Process input_data so that it is ready to be sent to our model.
# You should produce two variables:
# data_X - A sequence of length 500 which represents the converted review
# data_len - The length of the review
data_X, data_len = convert_and_pad(model.word_dict,
review_to_words(input_data),
pad=500)
# Using data_X and data_len we construct an appropriate input tensor. Remember
# that our model expects input data of the form 'len, review[500]'.
data_pack = np.hstack((data_len, data_X))
data_pack = data_pack.reshape(1, -1)
data = torch.from_numpy(data_pack)
data = data.to(device)
# Make sure to put the model into evaluation mode
model.eval()
# TODO: Compute the result of applying the model to the input data. The variable `result` should
# be a numpy array which contains a single integer which is either 1 or 0
output = model(data)
#print(output)
output = torch.round(output.squeeze())
output_temp = torch.tensor(output, requires_grad=True)
result = torch.round(output_temp.detach().numpy())
#print(result)
# Citations:
# https://github.com/udacity/deep-learning-v2-pytorch/blob/master/sentiment-rnn/Sentiment_RNN_Solution.ipynb
# https://stackoverflow.com/questions/55466298/pytorch-cant-call-numpy-on-variable-that-requires-grad-use-var-detach-num
return result
def predict_fn(input_data, model):
print('Inferring sentiment of input data.')
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
if model.word_dict is None:
raise Exception('Model has not been loaded properly, no word_dict.')
# TODO: Process input_data so that it is ready to be sent to our model.
# You should produce two variables:
# data_X - A sequence of length 500 which represents the converted review
# data_len - The length of the review
# using imported functions that already do this:
words_in_review = review_to_words(input_data)
words_in_review, length = convert_and_pad(model.word_dict, words_in_review)
data_X = words_in_review
data_len = length
# Using data_X and data_len we construct an appropriate input tensor. Remember
# that our model expects input data of the form 'len, review[500]'.
data_pack = np.hstack((data_len, data_X))
data_pack = data_pack.reshape(1, -1)
data = torch.from_numpy(data_pack)
data = data.to(device)
# Make sure to put the model into evaluation mode
model.eval()
# TODO: Compute the result of applying the model to the input data. The variable `result` should
# be a numpy array which contains a single integer which is either 1 or 0
#output = model(data).numpy()
output = model(data).detach().cpu().numpy()
#generates error without using detach! WHY?? //delete later
print(output)
result_real_rounded = np.round(output)
result = result_real_rounded.astype(np.int)
return result
def predict_fn(input_data, model):
print('Inferring sentiment of input data.')
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
if model.word_dict is None:
raise Exception('Model has not been loaded properly, no word_dict.')
# TODO: Process input_data so that it is ready to be sent to our model.
# You should produce two variables:
# data_X - A sequence of length 500 which represents the converted review
# data_len - The length of the review
data_X = None
data_len = None
data_X = review_to_words(input_data)
data_X, data_len = convert_and_pad(model.word_dict, data_X)
# Using data_X and data_len we construct an appropriate input tensor. Remember
# that our model expects input data of the form 'len, review[500]'.
data_pack = np.hstack((data_len, data_X))
data_pack = data_pack.reshape(1, -1)
data = torch.from_numpy(data_pack)
data = data.to(device)
# Make sure to put the model into evaluation mode
model.eval()
# TODO: Compute the result of applying the model to the input data. The variable `result` should
# be a numpy array which contains a single integer which is either 1 or 0
result = None
with torch.no_grad(
): #This provides us memory efficient. We don't need to calculate gradient to backward.
output = model.forward(data)
output = output.to('cpu')
result = int(np.round(output.numpy()))
#.numpy() provides retrieve tensor object as numpy array. I have taken this information from below link.
#https://discuss.pytorch.org/t/how-to-transform-variable-into-numpy/104
return result
def predict_fn(input_data, model):
print('Inferring sentiment of input data.')
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
if model.word_dict is None:
raise Exception('Model has not been loaded properly, no word_dict.')
# TODO: Process input_data so that it is ready to be sent to our model.
# You should produce two variables:
# data_X - A sequence of length 500 which represents the converted review
# data_len - The length of the review
#data_X = None
#data_len = None
# As we did in the "More testing" section of the "Sage Maker Projec.ipynb"
#test_review_words = review_to_words(test_review)
input_data = review_to_words(input_data)
#test_review_words, test_review_len = convert_and_pad(word_dict, test_review_words)
# Check whether word_dict exist
#print(list(model.word_dict)[:2])
data_X, data_len = convert_and_pad(model.word_dict, input_data)
# Using data_X and data_len we construct an appropriate input tensor. Remember
# that our model expects input data of the form 'len, review[500]'.
data_pack = np.hstack((data_len, data_X))
data_pack = data_pack.reshape(1, -1)
data = torch.from_numpy(data_pack)
data = data.to(device)
# Make sure to put the model into evaluation mode
model.eval()
# TODO: Compute the result of applying the model to the input data. The variable `result` should
# be a numpy array which contains a single integer which is either 1 or 0
result = np.array(int(model(data).detach().round()))
return result
def predict_fn(input_data, model):
print('Inferring sentiment of input data.')
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
if model.word_dict is None:
raise Exception('Model has not been loaded properly, no word_dict.')
# COMPLETED: Process input_data so that it is ready to be sent to our model.
# You should produce two variables:
# data_X - A sequence of length 500 which represents the converted review
# data_len - The length of the review
words_predict = review_to_words(input_data)
# convert_and_pad returns working_sentence, min(len(sentence), pad)
# default pad value is 500
data_X, data_len = convert_and_pad(model.word_dict, words_predict)
# Using data_X and data_len we construct an appropriate input tensor. Remember
# that our model expects input data of the form 'len, review[500]'.
data_pack = np.hstack((data_len, data_X))
data_pack = data_pack.reshape(1, -1)
data = torch.from_numpy(data_pack)
data = data.to(device)
# Make sure to put the model into evaluation mode
model.eval()
# COMPLETED: Compute the result of applying the model to the input data. The variable `result` should
# be a numpy array which contains a single integer which is either 1 or 0
# deactivate autograd engine
with torch.no_grad():
outputs = model(data)
# np.round() rounds the number
# .cpu() make sure the tensor is moved to memory accessible to the CPU
# .numpy() turns the tensor into ndarray for ease of computation
result = int(np.round(outputs.cpu().numpy()))
return result
def predict_fn(input_data, model):
print('Inferring sentiment of input data.')
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
if model.word_dict is None:
raise Exception('Model has not been loaded properly, no word_dict.')
data_pack = np.hstack(
convert_and_pad(model.word_dict, review_to_words(input_data))).reshape(
(1, -1))
data = torch.from_numpy(data_pack)
data = data.to(device)
# Make sure to put the model into evaluation mode
model.eval()
return int(np.round(model(data).detach().cpu().numpy()))
def predict_fn(input_data, model):
print('Inferring sentiment of input data.')
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
if model.word_dict is None:
raise Exception('Model has not been loaded properly, no word_dict.')
# TODO: Process input_data so that it is ready to be sent to our model.
# You should produce two variables:
# data_X - A sequence of length 500 which represents the converted review
# data_len - The length of the review
# First process the raw input data
reviewed_words = review_to_words(input_data)
word_dictionary = model.word_dict
# Then use the convert_and_pad function providing it the word dictionary from the model object and the processed "rvw"
data_X, data_len = convert_and_pad(word_dictionary, reviewed_words)
# Using data_X and data_len we construct an appropriate input tensor. Remember
# that our model expects input data of the form 'len, review[500]'.
data_pack = np.hstack((data_len, data_X))
data_pack = data_pack.reshape(1, -1)
data = torch.from_numpy(data_pack)
data = data.to(device)
# Make sure to put the model into evaluation mode
model.eval()
# TODO: Compute the result of applying the model to the input data. The variable `result` should
# be a numpy array which contains a single integer which is either 1 or 0
# Predict the Output by just passing the data into the model and calculating one forward step (prediction)
with torch.no_grad():
output = model.forward(data)
# Round the data (so that we only have either 0 or 1 in it)
output = output.numpy()
rounded_output = output.round()
return rounded_output
def predict_fn(input_data, model):
print('Inferring sentiment of input data.')
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
if model.word_dict is None:
raise Exception('Model has not been loaded properly, no word_dict.')
# TODO: Process input_data so that it is ready to be sent to our model.
# You should produce two variables:
# data_X - A sequence of length 500 which represents the converted review
# data_len - The length of the review
review_words = review_to_words(input_data)
review_words, review_length = convert_and_pad(model.word_dict,
review_words)
data_X = review_words
data_len = review_length
# Using data_X and data_len we construct an appropriate input tensor. Remember
# that our model expects input data of the form 'len, review[500]'.
data_pack = np.hstack((data_len, data_X))
data_pack = data_pack.reshape(1, -1)
data = torch.from_numpy(data_pack)
data = data.to(device)
# Make sure to put the model into evaluation mode
model.eval()
# TODO: Compute the result of applying the model to the input data. The variable `result` should
# be a numpy array which contains a single integer which is either 1 or 0
prediction = model(data).detach().cpu().numpy() #Move the value to cpu
print(prediction) #Printing the model's prediction
#Convert the prediction
result = np.asarray(prediction).astype(
np.int
) #Converting the prediction into a numpy array and changing the data type to int
return result
def predict_fn(input_data, model):
print('Inferring sentiment of input data.')
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
if model.word_dict is None:
raise Exception('Model has not been loaded properly, no word_dict.')
# TODO: Process input_data so that it is ready to be sent to our model.
# You should produce two variables:
# data_X - A sequence of length 500 which represents the converted review
# data_len - The length of the review
# word_dict_path = os.path.join(model_dir, 'word_dict.pkl')
processed_data = convert_and_pad(model.word_dict,
review_to_words(input_data))
# print(input_data)
# processed_data=input_data
data_X = processed_data[0]
data_len = processed_data[1]
# Using data_X and data_len we construct an appropriate input tensor. Remember
# that our model expects input data of the form 'len, review[500]'.
data_pack = np.hstack((data_len, data_X))
data_pack = data_pack.reshape(1, -1)
data = torch.from_numpy(data_pack)
data = data.to(device)
# Make sure to put the model into evaluation mode
model.eval()
# TODO: Compute the result of applying the model to the input data. The variable `result` should
# be a numpy array which cntains a single integer which is either 1 or 0
print("data input into the model is", data)
output = model(data)
print("output from the model is :", output)
result = np.around(output.detach().numpy()).astype('int')
return result
def predict_fn(input_data, model):
print('Inferring sentiment of input data.')
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
if model.word_dict is None:
raise Exception('Model has not been loaded properly, no word_dict.')
# Process input_data so that it is ready to be sent to our model.
# You should produce two variables:
# data_X - A sequence of length 500 which represents the converted review
# data_len - The length of the review
#data_X = [review_to_words(review) for review in input_data]
#data_X = convert_and_pad(word_dict, data_X, 500)
#data_len = 500
input_data = review_to_words(input_data)
data_X, data_len = convert_and_pad(model.word_dict, input_data)
# Using data_X and data_len we construct an appropriate input tensor. Remember
# that our model expects input data of the form 'len, review[500]'.
data_pack = np.hstack((data_len, data_X))
data_pack = data_pack.reshape(1, -1)
data = torch.from_numpy(data_pack)
data = data.to(device)
# Make sure to put the model into evaluation mode
model.eval()
# Compute the result of applying the model to the input data. The variable `result` should
# be a numpy array which contains a single integer which is either 1 or 0
result = float(model(data).detach().numpy())
if result >= 0.5:
result = 1
else:
result = 0
return np.array(result)
def predict_fn(input_data, model):
print('Inferring sentiment of input data.')
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
if model.word_dict is None:
raise Exception('Model has not been loaded properly, no word_dict.')
# TODO: Process input_data so that it is ready to be sent to our model.
# You should produce two variables:
# data_X - A sequence of length 500 which represents the converted review
# data_len - The length of the review
tokens = review_to_words(input_data)
data_X, data_len = convert_and_pad(model.word_dict, tokens)
print(tokens)
#print
#reshape function
data_pack = np.hstack((data_len, data_X))
data_pack = data_pack.reshape(1, -1)
data = torch.from_numpy(data_pack)
data = data.to(device)
print(data)
#final
model.eval()
#model.eval()
with torch.no_grad():
#print
print(data)
result = model(data).detach().numpy()
print(result)
return result
def input_handler(data, context):
""" Pre-process request input before it is sent to TensorFlow Serving REST API
Args:
data (obj): the request data, in format of dict or string
context (Context): an object containing request and configuration details
Returns:
(dict): a JSON-serializable dict that contains request body and headers
"""
print("calling input_handler")
print("data:", data)
# print("dir(data):", dir(data))
# print("data.read():", data.read().decode('utf-8'))
print("context:", context)
# Load the saved word_dict.
# print(os.system('pwd'))
# print(os.system('ls -Rla ./'))
# print(os.system('ls -Rla /opt/ml/model'))
word_dict_path = os.path.join(
'/opt/ml/model', 'word_dict.pkl'
) # model is saved at /opt/ml/model of the deployed endpoint
with open(word_dict_path, 'rb') as f:
word_dict = pickle.load(f)
print('word_dict:', word_dict)
if context.request_content_type == 'application/json':
d = data.read().decode('utf-8')
print('Decoded data:', d)
data_X, data_len = convert_and_pad(word_dict, review_to_words(d))
data_X = np.array(data_X)[np.newaxis]
data_X_json = json.dumps({'instances': data_X.tolist()})
print('data_X_json:', data_X_json)
return data_X_json
raise ValueError('{{"error": "unsupported content type {}"}}'.format(
context.request_content_type or "unknown"))
def predict_fn(input_data, model):
print('Inferring sentiment of input data.')
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
if model.word_dict is None:
raise Exception('Model has not been loaded properly, no word_dict.')
# TODO: Process input_data so that it is ready to be sent to our model.
# You should produce two variables:
# data_X - A sequence of length 500 which represents the converted review
# data_len - The length of the review
data_X, data_len = convert_and_pad(model.word_dict,
review_to_words(input_data))
# Using data_X and data_len we construct an appropriate input tensor. Remember
# that our model expects input data of the form 'len, review[500]'.
data_pack = np.hstack((data_len, data_X))
data_pack = data_pack.reshape(1, -1)
data = torch.from_numpy(data_pack)
data = data.to(device)
# Make sure to put the model into evaluation mode
model.eval()
# TODO: Compute the result of applying the model to the input data. The variable `result` should
# be a numpy array which contains a single integer which is either 1 or 0
# result: b"tensor(0.9998, device='cuda:0')"
# result: b"tensor(1.00000e-3 *\n 3.778, device='cuda:0')"
re = str(model(data))
res = re.split("(")
if "*" in res[1]:
result = np.asarray(0)
else:
tmp = [1 if float(res[1].split(",")[0]) > 0.50 else 0]
result = np.asarray(tmp[0])
return result
def predict_fn(input_data, model):
print('Inferring sentiment of input data.')
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
if model.word_dict is None:
raise Exception('Model has not been loaded properly, no word_dict.')
# TODO: Process input_data so that it is ready to be sent to our model.
# You should produce two variables:
# data_X - A sequence of length 500 which represents the converted review
# data_len - The length of the review
# from the imported utils, we get the list of sanetized words of the review, that is clean
# from html tags, punctuation, removes stopping words, and returns all to lowercase
words = review_to_words(review=input_data)
# now from the imported utils, match the first 500 sanetized words to the word_dict of the model
data_X, data_len = convert_and_pad(word_dict=model.word_dict,
sentence=words,
pad=500)
# Using data_X and data_len we construct an appropriate input tensor. Remember
# that our model expects input data of the form 'len, review[500]'.
data_pack = np.hstack((data_len, data_X))
data_pack = data_pack.reshape(1, -1)
data = torch.from_numpy(data_pack)
data = data.to(device)
# Make sure to put the model into evaluation mode
model.eval()
# TODO: Compute the result of applying the model to the input data. The variable `result` should
# be a numpy array which contains a single integer which is either 1 or 0
out = model.forward(data)
result = np.array(round(float(out)))
return result
def predict_fn(input_data, model):
print('Inferring sentiment of input data.')
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
if model.word_dict is None:
raise Exception('Model has not been loaded properly, no word_dict.')
data_X, data_len = convert_and_pad(model.word_dict,
review_to_words(input_data))
data_pack = np.hstack((data_len, data_X))
data_pack = data_pack.reshape(1, -1)
data = torch.from_numpy(data_pack)
data = data.to(device)
model.eval()
with torch.no_grad():
output = model.forward(data)
return np.round(output.numpy())
def predict_fn(input_data, model):
"""
Make prediction on input data thanks to model
"""
# Begin predicting:
print("Inferring sentiment of input data...")
# Determine the device:
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# Check model word dictionary presence:
if model.word_dict is None:
raise Exception("Model has not been loaded properly: no word_dict")
# Process input_data so that it is ready to be sent to our model:
data_x, data_len = convert_and_pad(model.word_dict,
review_to_words(input_data))
# Construct an appropriate input tensor:
data_pack = np.hstack((data_len, data_x))
data_pack = data_pack.reshape(1, -1)
data = torch.from_numpy(data_pack)
data = data.to(device)
# Make sure to put the model into evaluation mode:
model.eval()
# Apply model to input tensor:
output_data = model.forward(data)
# Transform output into a numpy array which contains a single integer, 1 or 0:
if torch.cuda.is_available():
# NumPy doesn't support CUDA:
output_data.to('cpu')
result = int(np.round(output_data.detach().numpy()))
# Return prediction:
return result
def predict_fn(input_data, model):
print('Inferring sentiment of input data.')
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
if model.word_dict is None:
raise Exception('Model has not been loaded properly, no word_dict.')
# TODO: Process input_data so that it is ready to be sent to our model.
# You should produce two variables:
# data_X - A sequence of length 500 which represents the converted review
# data_len - The length of the review
words = review_to_words(input_data)
data_X, data_len = convert_and_pad(model.word_dict, words)
# Using data_X and data_len we construct an appropriate input tensor. Remember
# that our model expects input data of the form 'len, review[500]'.
data_pack = np.hstack((data_len, data_X))
data_pack = data_pack.reshape(1, -1)
data = torch.from_numpy(data_pack)
data = data.to(device)
# Make sure to put the model into evaluation mode
model.eval()
# TODO: Compute the result of applying the model to the input data. The variable `result` should
# be a numpy array which contains a single integer which is either 1 or 0
# ideas from https://github.com/AAnoosheh/MNIST_test/blob/master/test.py
# solution with detach() taken from CloudWatch logs
# solution with numpy() found in https://stackoverflow.com/questions/34097281/how-can-i-convert-a-tensor-into-a-numpy-array-in-tensorflow
with torch.no_grad():
output = model.forward(data).round().detach().numpy()
result = int(float(output))
return result
def predict_fn(input_data, model):
print('Inferring sentiment of input data.')
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
if model.word_dict is None:
raise Exception('Model has not been loaded properly, no word_dict.')
# TODO: Process input_data so that it is ready to be sent to our model.
# You should produce two variables:
# data_X - A sequence of length 500 which represents the converted review
# data_len - The length of the review
input_data = review_to_words(input_data)
data_X, data_len = convert_and_pad(model.word_dict, input_data)
# Using data_X and data_len we construct an appropriate input tensor. Remember
# that our model expects input data of the form 'len, review[500]'.
data_pack = np.hstack((data_len, data_X))
data_pack = data_pack.reshape(1, -1)
data = torch.from_numpy(data_pack)
data = data.to(device)
model.eval()
# TODO: Compute the result of applying the model to the input data. The variable `result` should
# be a numpy array which contains a single integer which is either 1 or 0
# Note: RuntimeError: Can't call numpy() on Variable that requires grad. Use var.detach().numpy() instead.
# We need to remove grad in order to return a numpy array
with torch.no_grad():
result = model(data).numpy()
result = np.round(result)
return result
def predict_fn(input_data, model):
print('Inferring sentiment of input data.')
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
if model.word_dict is None:
raise Exception('Model has not been loaded properly, no word_dict.')
# TODO: Process input_data so that it is ready to be sent to our model.
# You should produce two variables:
# data_X - A sequence of length 500 which represents the converted review
# data_len - The length of the review
# converts string into list of stemmed words with html syntax removed
clean_list = review_to_words(input_data)
#encodes list of words into a list of numbers, padded with zeros to standardize length
data_X, data_len = convert_and_pad(model.word_dict, clean_list)
# Using data_X and data_len we construct an appropriate input tensor. Remember
# that our model expects input data of the form 'len, review[500]'.
data_pack = np.hstack((data_len, data_X))
data_pack = data_pack.reshape(1, -1)
data = torch.from_numpy(data_pack)
data = data.to(device)
# Make sure to put the model into evaluation mode
model.eval()
# TODO: Compute the result of applying the model to the input data. The variable `result` should
# be a numpy array which contains a single integer which is either 1 or 0
output = np.round(model(data).detach().numpy()).astype(np.int)
return output