python - Pandas DataFrame and Keras

Question

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

Answer 1

None is the number of expected rows that goes into training therefore you can't define it. Also Keras needs a numpy array as input and not a pandas dataframe. First convert the df to a numpy array with df.values and then do np.reshape((-1, 4834)). Note that you should use np.float32. This is important if you train it on GPU.