I'm trying to perform a sentiment analysis in Python using Keras. To do so, I need to do a word embedding of my texts. The problem appears when I try to fit the data to my model:
model_1 = Sequential()
model_1.add(Embedding(1000,32, input_length = X_train.shape[0]))
model_1.add(Flatten())
model_1.add(Dense(250, activation='relu'))
model_1.add(Dense(1, activation='sigmoid'))
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
The shape of my train data is
(4834,)
And is a Pandas series object. When I try to fit my model and validate it with some other data I get this error:
model_1.fit(X_train, y_train, validation_data=(X_test, y_test), epochs=2, batch_size=64, verbose=2)
ValueError: Error when checking model input: expected
embedding_1_input to have shape (None, 4834) but got array with shape
(4834, 1)
How can I reshape my data to make it suited for Keras? I've been trying with np.reshape but I cannot place None elements with that function.
Thanks in advance
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