I have worked with text classification using Glove and CNN and found the problem below:
File "c:\programfiles_anaconda\anaconda3\envs\math_stat_class\lib\site-packages\tensorflow\python\framework\ops.py", line 1657, in _create_c_op
raise ValueError(str(e))
ValueError: Negative dimension size caused by subtracting 5 from 1 for '{{node max_pooling1d_9/MaxPool}} = MaxPool[T=DT_FLOAT, data_format="NHWC", ksize=[1, 5, 1, 1], padding="VALID", strides=[1, 5, 1, 1]](max_pooling1d_9/ExpandDims)' with input shapes: [?,1,1,128].
EMBEDDING_DIM = 100
embeddings_index = {}
f = open(glove_path, encoding='utf-8')
for line in f:
values = line.split()
word = values[0]
coefs = np.asarray(values[1:], dtype='float32')
embeddings_index[word] = coefs
f.close()
print('Found %s word vectors.' % len(embeddings_index))
embedding_matrix = np.zeros((len(word_index) + 1, EMBEDDING_DIM))
for word, i in word_index.items():
embedding_vector = embeddings_index.get(word)
if embedding_vector is not None:
# words not found in embedding index will be all-zeros.
embedding_matrix[i] = embedding_vector
# apply embedding matrix into an Embedding layer
# trainable=False to prevent the weights from being updated during training
embedding_layer = Embedding(len(word_index) + 1,
EMBEDDING_DIM,
weights=[embedding_matrix],
input_length=MAX_SEQUENCE_LENGTH,
trainable=False)
sequence_input = Input(shape=(MAX_SEQUENCE_LENGTH,), dtype='int32')
embedded_sequences = embedding_layer(sequence_input)
x = Conv1D(128, 5, activation='relu')(embedded_sequences)
print("x shape = ", x)
x = MaxPooling1D(5)(x)
print("x shape = ", x)
x = Conv1D(128, 5, activation='relu')(x)
print("x shape = ", x)
#-----This line below produced error-----
x = MaxPooling1D(5)(x) #Error this line
#-----This line above produced error-----
print("x shape = ", x)
x = Conv1D(128, 5, activation='relu')(x)
print("x shape = ", x)
x = MaxPooling1D(35)(x) # global max pooling
print("x shape = ", x)
x = Flatten()(x)
x = Dense(128, activation='relu')(x)
preds = Dense(len(labels_index), activation='softmax')(x)
model = Model(sequence_input, preds)
model.compile(loss='categorical_crossentropy',
optimizer='rmsprop',
metrics=['acc'])
# Learning
model.fit(X_train, y_train, validation_data=(X_val, y_val),
epochs=2, batch_size=128)
1) Are there some issues/problems with Glove input?
2) Conv1D:
3) MaxPooling1D:
4) I currently use keras on tensorflow 2.20 and python 3.6
However, I could not figure out any better way to do. May I have your suggestions?
Two things that come to my mind: your max-pooling layers are reducing the size of the input to the next convolutional layers every time and eventually the size is too small to run another max-pooling operation. Try running
tf.print(model.summary)
after each max-pooling operation and you will quickly find out that your tensor cannot be further reduced. You can then consider using a different pool_size
in your max-pooling layers.
The second thing I notice (I am not sure if it is intentional), but MaxPooling1D != Global Max Pooling . Keras supports both operations . Take a look at the documentation.
On a side note, sentence classification with CNNs was widely popularized by the work of Yoon Kim. In his work, he shows that global max-pooling operations perform much better than striding max-pooling operations in sentence classification (when using word embeddings, as you are doing).
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