I am trying to make a NLP multi-class sentiment classifier where it takes in sentences as input and classifies them into three classes (negative, neutral and positive). However, when training the model, I run into the error where my logits (None, 3) are not the same size as my labels (None, 1) and the model can't begin training.
My model is a multi-class classifier and not a multi-label classifier since it is only predicting one label per object. I made sure that my last layer had an output of 3 and had the activation = 'softmax'. This should be correct from what I have searched online so I think that the problem lies with my labels.
Currently, my labels have a dimension of (None, 1) since I mapped each class to a unique integer and passed this as my test and train y values (which are in the form of one dimensional numpy array.
Right now I am confused if I have change the dimensions of this array to match the output dimensions and how to go about doing it.
import os
import sys
import tensorflow as tf
import numpy as np
import pandas as pd
from tensorflow import keras
from tensorflow.keras.preprocessing.text import Tokenizer
from tensorflow.keras.preprocessing.sequence import pad_sequences
from keras.optimizers import SGD
device_name = tf.test.gpu_device_name()
if len(device_name) > 0:
print("Found GPU at: {}".format(device_name))
else:
device_name = "/device:CPU:0"
print("No GPU, using {}.".format(device_name))
# Load dataset into a dataframe
train_data_path = "/content/drive/MyDrive/ML Datasets/tweet_sentiment_analysis/train.csv"
test_data_path = "/content/drive/MyDrive/ML Datasets/tweet_sentiment_analysis/test.csv"
train_df = pd.read_csv(train_data_path, encoding='unicode_escape')
test_df = pd.read_csv(test_data_path, encoding='unicode_escape').dropna()
sentiment_types = ('neutral', 'negative', 'positive')
train_df['sentiment'] = train_df['sentiment'].astype('category')
test_df['sentiment'] = test_df['sentiment'].astype('category')
train_df['sentiment_cat'] = train_df['sentiment'].cat.codes
test_df['sentiment_cat'] = test_df['sentiment'].cat.codes
train_y = np.array(train_df['sentiment_cat'])
test_y = np.array(test_df['sentiment_cat'])
# Function to convert df into a list of strings
def convert_to_list(df, x):
selected_text_list = []
labels = []
for index, row in df.iterrows():
selected_text_list.append(str(row[x]))
labels.append(str(row['sentiment']))
return np.array(selected_text_list), np.array(labels)
train_sentences, train_labels = convert_to_list(train_df, 'selected_text')
test_sentences, test_labels = convert_to_list(test_df, 'text')
# Instantiate tokenizer and create word_index
tokenizer = Tokenizer(num_words=1000, oov_token='<oov>')
tokenizer.fit_on_texts(train_sentences)
word_index = tokenizer.word_index
# Convert sentences into a sequence
train_sequence = tokenizer.texts_to_sequences(train_sentences)
test_sequence = tokenizer.texts_to_sequences(test_sentences)
# Padding sequences
pad_test_seq = pad_sequences(test_sequence, padding='post')
max_len = pad_test_seq[0].size
pad_train_seq = pad_sequences(train_sequence, padding='post', maxlen=max_len)
model = tf.keras.Sequential([
tf.keras.layers.Embedding(10000, 64, input_length=max_len),
tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(64, return_sequences=True)),
tf.keras.layers.GlobalAveragePooling1D(),
tf.keras.layers.Dense(64, activation='relu'),
tf.keras.layers.Dense(3, activation='softmax')
])
with tf.device(device_name):
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
num_epochs = 10
with tf.device(device_name):
history = model.fit(pad_train_seq, train_y, epochs=num_epochs, validation_data=(pad_test_seq, test_y), verbose=2)
Here is the error:
ValueError Traceback (most recent call last)
<ipython-input-28-62f3c6445887> in <module>
2
3 with tf.device(device_name):
----> 4 history = model.fit(pad_train_seq, train_y, epochs=num_epochs, validation_data=(pad_test_seq, test_y), verbose=2)
1 frames
/usr/local/lib/python3.8/dist-packages/keras/engine/training.py in tf__train_function(iterator)
13 try:
14 do_return = True
---> 15 retval_ = ag__.converted_call(ag__.ld(step_function), (ag__.ld(self), ag__.ld(iterator)), None, fscope)
16 except:
17 do_return = False
ValueError: in user code:
File "/usr/local/lib/python3.8/dist-packages/keras/engine/training.py", line 1051, in train_function *
return step_function(self, iterator)
File "/usr/local/lib/python3.8/dist-packages/keras/engine/training.py", line 1040, in step_function **
outputs = model.distribute_strategy.run(run_step, args=(data,))
File "/usr/local/lib/python3.8/dist-packages/keras/engine/training.py", line 1030, in run_step **
outputs = model.train_step(data)
File "/usr/local/lib/python3.8/dist-packages/keras/engine/training.py", line 890, in train_step
loss = self.compute_loss(x, y, y_pred, sample_weight)
File "/usr/local/lib/python3.8/dist-packages/keras/engine/training.py", line 948, in compute_loss
return self.compiled_loss(
File "/usr/local/lib/python3.8/dist-packages/keras/engine/compile_utils.py", line 201, in __call__
loss_value = loss_obj(y_t, y_p, sample_weight=sw)
File "/usr/local/lib/python3.8/dist-packages/keras/losses.py", line 139, in __call__
losses = call_fn(y_true, y_pred)
File "/usr/local/lib/python3.8/dist-packages/keras/losses.py", line 243, in call **
return ag_fn(y_true, y_pred, **self._fn_kwargs)
File "/usr/local/lib/python3.8/dist-packages/keras/losses.py", line 1930, in binary_crossentropy
backend.binary_crossentropy(y_true, y_pred, from_logits=from_logits),
File "/usr/local/lib/python3.8/dist-packages/keras/backend.py", line 5283, in binary_crossentropy
return tf.nn.sigmoid_cross_entropy_with_logits(labels=target, logits=output)
ValueError: `logits` and `labels` must have the same shape, received ((None, 3) vs (None, 1)).
CodePudding user response：
my logits (None, 3) are not the same size as my labels (None, 1) I made sure that my last layer had an output of 3 and had the activation = 'softmax' my labels have a dimension of (None, 1) since I mapped each class to a unique integer
The key concept you are missing is that you need to one-hot encode your labels (after assigning integers to them - see below).
So your model, after the softmax, is spitting out three values: how probable each of your labels is. E.g. it might say A is 0.6, B is 0.1, and C is 0.3. If the correct answer is C, then it needs to see that correct answer as 0, 0, 1. It can then say that its prediction for A is 0.6 - 0 = 0.6 wrong, B is 0.1 - 0 = 0.1 wrong, and C is 0.3 - 1 = -0.7 wrong.
Theoretically you can go from a string label directly to a one-hot encoding. But it seems Tensorflow needs the labels to first be encoded as integers, and then that is one-hot encoded.
https://www.tensorflow.org/api_docs/python/tf/keras/layers/CategoryEncoding#examples says to use:
tf.keras.layers.CategoryEncoding(num_tokens=3, output_mode="one_hot")
Also see https://stackoverflow.com/a/69791457/841830 (the higher-voted answer there is from 2019, so applies to TensorFlow v1 I think). And searching for "tensorflow one-hot encoding" will bring up plenty of tutorials and examples.
CodePudding user response：
The issue here was indeed due to the shape of my labels not being the same as logits. Logits were of shape (3) since they contained a float for the probability of each of the three classes that I wanted to predict. Labels were originally of shape (1) since it only contained one int.
To solve this, I used one-hot encoding which turned all labels into a shape of (3) and this solved the problem. Used the keras.utils.to_categorical() function to do so.
sentiment_types = ('negative', 'neutral', 'positive')
train_df['sentiment'] = train_df['sentiment'].astype('category')
test_df['sentiment'] = test_df['sentiment'].astype('category')
# Turning labels from strings to int
train_sentiment_cat = train_df['sentiment'].cat.codes
test_sentiment_cat = test_df['sentiment'].cat.codes
# One-hot encoding
train_y = to_categorical(train_sentiment_cat)
test_y = to_categorical(test_sentiment_cat)