I am trying with SVC classifier to classify text.
#self.vectorizer = HashingVectorizer(non_negative=True)
#self.vectorizer = TfidfVectorizer(sublinear_tf=True, max_df=0.5, stop_words='english')
self.hasher = FeatureHasher(input_type='string',non_negative=True)
from sklearn.svm import SVC
self.clf = SVC(probability=True)
for text in self.data_train.data:
text = self.modifyQuery(text.decode('utf-8','ignore'))
training_data.append(text)
raw_X = (self.token_ques(text) for text in training_data)
#X_train = self.vectorizer.transform(training_data)
X_train = self.hasher.transform(raw_X)
y_train = self.data_train.target
self.clf.fit(X_train, y_train)
test classifier:
raw_X = (self.token_ques(text) for text in test_data)
X_test = self.hasher.transform(raw_X)
#X_test = self.vectorizer.transform(test_data)
pred = self.clf.predict(X_test)
print("pred=>", pred)
self.categories = self.data_train.target_names
for doc, category in zip(test_data, pred):
print('%r => %s' % (doc, self.categories[category]))
index = 1
predict_prob = self.clf.predict_proba(X_test)
for doc, category_list in zip(test_data, predict_prob):
#print values
I tried with hashing, feature, tfidf vectorizer but still it gives wrong answer for all queries (class with highest datasize comes as answer). While using naive bayes it gives correct result as per class and input query.
Am I doing anything wrong in code?
Update
I have total 8 classes, and each class having 100-200 lines of sentences. One class with 480 lines. This class always comes as a answer currently
Related
I have been trying to fine tune a BERT model to give response sentences like a character based on input sentences but I am getting a rather odd error every time . the code is
`
Here sourcetexts is a list of sentences that give the context and target_text is a list of sentences that give response to context statments
from transformers import AutoModel, AutoTokenizer
model = AutoModel.from_pretrained("bert-base-cased").to(device)
tokenizer = AutoTokenizer.from_pretrained("bert-base-cased")
input_ids = \[\]
output_ids = \[\]
for i in range (0 , len(source_text):
input_ids.append(tokenizer.encode(source_texts\[i\], return_tensors="pt"))
output_ids.append(tokenizer.encode(target_texts\[i\], return_tensors="pt"))
import torch
device = torch.device("cuda")
from transformers import BertForMaskedLM, AdamW
model = BertForMaskedLM.from_pretrained("bert-base-cased")
optimizer = AdamW(model.parameters(), lr=1e-5)
loss_fn = torch.nn.CrossEntropyLoss()
def train(input_id, output_id):
input_id = input_id.to(device)
output_id = output_id.to(device)
model.zero_grad()
logits, _ = model(input_id, labels=output_id)
# Compute the loss
loss = loss_fn(logits.view(-1, logits.size(-1)), output_id.view(-1))
loss.backward()
optimizer.step()
return loss.item()
for epoch in range(50):
\# Train the model on the training dataset
train_loss = 0.0
for input_sequences, output_sequences in zip(input_ids, output_ids):
input_sequences = input_sequences.to(device)
output_sequences = output_sequences.to(device)
train_loss += train(input_sequences, output_sequences)
This is the Error that I am getting
Any help would be really appreciated .
Pls help!!
Hi i saw your code but you didn't move your model to GPU, only the inputs, pytorch by default is on CPU
import torch
device = torch.device('cuda')
model = BertForMaskedLM.from_pretrained("bert-base-cased")
model.to(device)
I'm trying to follow the huggingface tutorial on fine tuning a masked language model (masking a set of words randomly and predicting them). But they assume that the dataset is in their system (can load it with from datasets import load_dataset; load_dataset("dataset_name")). However, my input dataset is a long string:
text = "This is an attempt of a great example. "
dataset = text * 3000
I followed their approach and tokenized each it:
from transformers import AutoTokenizer
from transformers import AutoModelForMaskedLM
import torch
from transformers import DataCollatorForLanguageModeling
model_checkpoint = "distilbert-base-uncased"
model = AutoModelForMaskedLM.from_pretrained(model_checkpoint)
tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)
def tokenize_long_text(tokenizer, long_text):
individual_sentences = long_text.split('.')
tokenized_sentences_list = tokenizer(individual_sentences)['input_ids']
tokenized_sequence = [x for xs in tokenized_sentences_list for x in xs]
return tokenized_sequence
tokenized_sequence = tokenize_long_text(tokenizer, long_text)
Following by chunking it into equal length segments:
def chunk_long_tokenized_text(tokenizer_text, chunk_size):
# Compute length of long tokenized texts
total_length = len(tokenizer_text)
# We drop the last chunk if it's smaller than chunk_size
total_length = (total_length // chunk_size) * chunk_size
return [tokenizer_text[i : i + chunk_size] for i in range(0, total_length, chunk_size)]
chunked_sequence = chunk_long_tokenized_text(tokenized_sequence, 30)
Created a data collator for random masking:
data_collator = DataCollatorForLanguageModeling(tokenizer=tokenizer, mlm_probability=0.15) # expects a list of dicts, where each dict represents a single chunk of contiguous text
Example of how it works:
d = {}
d['input_ids'] = chunked_sequence[0]
d
>>>{'input_ids': [101,
2023,
2003,
1037,
2307,
103,...
for chunk in data_collator([ d ])["input_ids"]:
print(f"\n'>>> {tokenizer.decode(chunk)}'")
>>>'>>> [CLS] this is a great [MASK] [SEP] [CLS] this is a great [MASK] [SEP] [CLS] this is a great [MASK] [SEP] [CLS] this is a great [MASK] [SEP] [CLS] this'
However, the remaining steps (which I believe is just the training component) seem to only work using their trainer method, which can only take their dataset.
How can this work with a dataset in the form of a string?
I created a deep learning model, and I want to check the performance of the model by using predict_generator. I am using the following code which compares the images' labels with the predicted classes and then returns the prediction error.
validation_generator = validation_datagen.flow_from_directory(
validation_dir,
target_size=(image_size, image_size),
batch_size=val_batchsize,
class_mode='categorical',
shuffle=False)
# Get the filenames from the generator
fnames = validation_generator.filenames
# Get the ground truth from generator
ground_truth = validation_generator.classes
# Get the label to class mapping from the generator
label2index = validation_generator.class_indices
# Getting the mapping from class index to class label
idx2label = dict((v,k) for k,v in label2index.items())
# Get the predictions from the model using the generator
predictions = model.predict_generator(validation_generator, steps=validation_generator.samples/validation_generator.batch_size,verbose=1)
predicted_classes = np.argmax(predictions,axis=1)
errors = np.where(predicted_classes != ground_truth)[0]
print("No of errors = {}/{}".format(len(errors),validation_generator.samples))
# Show the errors
for i in range(len(errors)):
pred_class = np.argmax(predictions[errors[i]])
pred_label = idx2label[pred_class]
title = 'Original label:{}, Prediction :{}, confidence : {:.3f}'.format(
fnames[errors[i]].split('/')[0],
pred_label,
predictions[errors[i]][pred_class])
original = load_img('{}/{}'.format(validation_dir,fnames[errors[i]]))
plt.figure(figsize=[7,7])
plt.axis('off')
plt.title(title)
plt.imshow(original)
plt.show()
validation_generator.classes is arranged but predicted_classes is not arranged.
I take the code from here https://www.learnopencv.com/keras-tutorial-fine-tuning-using-pre-trained-models/
How can I prevent predict_generator from shuffling data?
I have a classification model in TF and can get a list of probabilities for the next class (preds). Now I want to select the highest element (argmax) and display its class label.
This may seems silly, but how can I get the class label that matches a position in the predictions tensor?
feed_dict={g['x']: current_char}
preds, state = sess.run([g['preds'],g['final_state']], feed_dict)
prediction = tf.argmax(preds, 1)
preds gives me a vector of predictions for each class. Surely there must be an easy way to just output the most likely class (label)?
Some info about my model:
x = tf.placeholder(tf.int32, [None, num_steps], name='input_placeholder')
y = tf.placeholder(tf.int32, [None, 1], name='labels_placeholder')
batch_size = batch_size = tf.shape(x)[0]
x_one_hot = tf.one_hot(x, num_classes)
rnn_inputs = [tf.squeeze(i, squeeze_dims=[1]) for i in
tf.split(x_one_hot, num_steps, 1)]
tmp = tf.stack(rnn_inputs)
print(tmp.get_shape())
tmp2 = tf.transpose(tmp, perm=[1, 0, 2])
print(tmp2.get_shape())
rnn_inputs = tmp2
with tf.variable_scope('softmax'):
W = tf.get_variable('W', [state_size, num_classes])
b = tf.get_variable('b', [num_classes], initializer=tf.constant_initializer(0.0))
rnn_outputs = rnn_outputs[:, num_steps - 1, :]
rnn_outputs = tf.reshape(rnn_outputs, [-1, state_size])
y_reshaped = tf.reshape(y, [-1])
logits = tf.matmul(rnn_outputs, W) + b
predictions = tf.nn.softmax(logits)
A prediction is an array of n types of classes(labels). It represents the model's "confidence" that the image corresponds to each of its classes(labels). You can check which label has the highest confidence value by using:
prediction = np.argmax(preds, 1)
After getting this highest element index using (argmax function) out of other probabilities, you need to place this index into class labels to find the exact class name associated with this index.
class_names[prediction]
Please refer to this link for more understanding.
You can use tf.reduce_max() for this. I would refer you to this answer.
Let me know if it works - will edit if it doesn't.
Mind that there are sometimes several ways to load a dataset. For instance with fashion MNIST the tutorial could lead you to use load_data() and then to create your own structure to interpret a prediction. However you can also load these data by using tensorflow_datasets.load(...) like here after installing tensorflow-datasets which gives you access to some DatasetInfo. So for instance if your prediction is 9 you can tell it's a boot with:
import tensorflow_datasets as tfds
_, ds_info = tfds.load('fashion_mnist', with_info=True)
print(ds_info.features['label'].names[9])
When you use softmax, the labels you train the model on are either numbers 0..n or one-hot encoded values. So if original labels of your data are let's say string names, you must map them to integers first and keep the mapping as a variable (such as 0 -> "apple", 1 -> "orange", 2 -> "pear" ...).
When using integers (with loss='sparse_categorical_crossentropy'), you get predictions as an array of probabilities, you just find the array index with the max value. You can use this predicted index to reverse-map to your label:
predictedIndex = np.argmax(predictions) // 2
predictedLabel = indexToLabelMap[predictedIndex] // "pear"
If you use one-hot encoded labels (with loss='categorical_crossentropy'), the predicted index corresponds with the "hot" index of your label.
Just for reference, I needed this info when I was working with MNIST dataset used in Google's Machine learning crash course. There is also a good classification tutorial in the Tensorflow docs.
I am using a kNN to do some classification of labeled images. After my classification is done, I am outputting a confusion matrix. I noticed that one label, bottle was being applied incorrectly more often.
I removed the label and tested again, but then noticed that another label, shoe was being applied incorrectly, but was fine last time.
There should be no normalization, so I'm unsure what is causing this behavior. Testing showed it continued no matter how many labels I removed.
Not totally sure how much code to post, so I'll put some things that should be relevant and pastebin the rest.
def confusionMatrix(classifier, train_DS_X, train_DS_y, test_DS_X, test_DS_y):
# Will output a confusion matrix graph for the predicion
y_pred = classifier.fit(train_DS_X, train_DS_y).predict(test_DS_X)
labels = set(set(train_DS_y) | set(test_DS_y))
def plot_confusion_matrix(cm, title='Confusion matrix', cmap=plt.cm.Blues):
plt.imshow(cm, interpolation='nearest', cmap=cmap)
plt.title(title)
plt.colorbar()
tick_marks = np.arange(len(labels))
plt.xticks(tick_marks, labels, rotation=45)
plt.yticks(tick_marks, labels)
plt.tight_layout()
plt.ylabel('True label')
plt.xlabel('Predicted label')
# Compute confusion matrix
cm = confusion_matrix(test_DS_y , y_pred)
np.set_printoptions(precision=2)
print('Confusion matrix, without normalization')
#print(cm)
plt.figure()
plot_confusion_matrix(cm)
# Normalize the confusion matrix by row (i.e by the number of samples
# in each class)
cm_normalized = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]
print('Normalized confusion matrix')
#print(cm_normalized)
plt.figure()
plot_confusion_matrix(cm_normalized, title='Normalized confusion matrix')
plt.show()
Relevant Code from Main Function:
# Select training and test data
PCA = decomposition.PCA(n_components=.95)
zscorer = ZScoreMapper(param_est=('targets', ['rest']), auto_train=False)
DS = getVoxels (1, .5)
train_DS = DS[0]
test_DS = DS[1]
# Apply PCA and ZScoring
train_DS = processVoxels(train_DS, True, zscorer, PCA)
test_DS = processVoxels(test_DS, False, zscorer, PCA)
print 3*"\n"
# Select the desired features
# If selecting samples or PCA, that must be the only feature
featuresOfInterest = ['pca']
trainDSFeat = selectFeatures(train_DS, featuresOfInterest)
testDSFeat = selectFeatures(test_DS, featuresOfInterest)
train_DS_X = trainDSFeat[0]
train_DS_y = trainDSFeat[1]
test_DS_X = testDSFeat[0]
test_DS_y = testDSFeat[1]
# Optimization of neighbors
# Naively searches for local max starting at numNeighbors
lastScore = 0
lastNeightbors = 1
score = .0000001
numNeighbors = 5
while score > lastScore:
lastScore = score
lastNeighbors = numNeighbors
numNeighbors += 1
#Classification
neigh = neighbors.KNeighborsClassifier(n_neighbors=numNeighbors, weights='distance')
neigh.fit(train_DS_X, train_DS_y)
#Testing
score = neigh.score(test_DS_X,test_DS_y )
# Confusion Matrix Output
neigh = neighbors.KNeighborsClassifier(n_neighbors=lastNeighbors, weights='distance')
confusionMatrix(neigh, train_DS_X, train_DS_y, test_DS_X, test_DS_y)
Pastebin: http://pastebin.com/U7yTs3vs
The issue was in part the result of my axis being mislabeled, when I thought I was removing the faulty label I was in actuality just removing a random label, meaning the faulty data was still being analyzed. Fixing the axis and removing the faulty label which was actually rest yielded:
The code I changed is:
cm = confusion_matrix(test_DS_y , y_pred, labels)
Basically I manually set the ordering based on my list of ordered labels.