received ((None, 2) vs (None, 5))

A small summary of classification problems about running deep learning models with keras.

 

What I got was a 2-category model, which needs to be changed to 5 categories. I didn't have a clue at first, so I checked around and solved the problem, and summarized it for later viewing.

python - Tensorflow ValueError: logits and labels must have the same shape ((None, 2) vs (None, 1)) - Stack Overflow

The above is the post I found that can solve my problem,

I'm new to Machine Learning, thought I'll start with keras. Here I'm classifying movie reviews as three class classification (positive as 1, neutral as 0 and negative as -1) using binary crossentropy. So, when I'm trying to wrap my keras model with tensorflow estimator, I get the error.
The code is as follows:

 It probably means that he wants to run a three-category model, and when he gets the model in hand to run, an error is reported. The code is as follows:

import tensorflow as tf
import numpy as np
import pandas as pd
import numpy as K

csvfilename_train = 'train(cleaned).csv'
csvfilename_test = 'test(cleaned).csv'

# Read .csv files as pandas dataframes
df_train = pd.read_csv(csvfilename_train)
df_test = pd.read_csv(csvfilename_test)

train_sentences  = df_train['Comment'].values
test_sentences  = df_test['Comment'].values

# Extract labels from dataframes
train_labels = df_train['Sentiment'].values
test_labels = df_test['Sentiment'].values

vocab_size = 10000
embedding_dim = 16
max_length = 30
trunc_type = 'post'
oov_tok = '<OOV>'

from tensorflow.keras.preprocessing.text import Tokenizer
from tensorflow.keras.preprocessing.sequence import pad_sequences

tokenizer = Tokenizer(num_words = vocab_size, oov_token = oov_tok)
tokenizer.fit_on_texts(train_sentences)
word_index = tokenizer.word_index
sequences = tokenizer.texts_to_sequences(train_sentences)
padded = pad_sequences(sequences, maxlen = max_length, truncating = trunc_type)

test_sequences = tokenizer.texts_to_sequences(test_sentences)
test_padded = pad_sequences(test_sequences, maxlen = max_length)

model = tf.keras.Sequential([
    tf.keras.layers.Embedding(vocab_size, embedding_dim, input_length = max_length),
    tf.keras.layers.Flatten(),
    tf.keras.layers.Dense(6, activation = 'relu'),
    tf.keras.layers.Dense(2, activation = 'sigmoid'),
])
model.compile(loss = 'binary_crossentropy', optimizer = 'adam', metrics = ['accuracy'])

num_epochs = 10
model.fit(padded, train_labels, epochs = num_epochs, validation_data = (test_padded, test_labels))

 Error message:

ValueError: logits and labels must have the same shape ((None, 2) vs (None, 1))

Here's my enthusiastic reply:

 It pointed out the problem at once. One is that the binary classification of loss should be changed to multi-classification, and the other is that the final Dense layer should be changed to 3, and the label should be converted to one-hot.

Change binary_crossentropy in loss to categorical_crossentropy

model.compile(loss = 'categorical_crossentropy', optimizer = 'adam', metrics = ['accuracy'])

Then the number of layers of the last Dense layer is changed to 3

tf.keras.layers.Dense(3, activation = 'sigmoid'),

There is also an article by an older brother that is also of great reference value, so I posted it below.

tensorflow2.0 uses its own functions to find precision and recall (solve Shapes (None, 10) and (None, 1) are incompatible)

The role of the Dense layer

In keras, it is equivalent to a fully connected layer, which receives the output of the previous Dense as the input of the next Dense, and the last Dense is responsible for the output.

that's all.