def addSpaces(text, minLength):
while(len(text) < minLength):
text += " "
return text
def convertToTokens(text):
return [ord(token) for token in text]
def buildExamples(text, paddedLength):
trainExamples = []
for i in range(len(text)):
trainExample = addSpaces(text[:i], paddedLength)
trainExamples.append(convertToTokens(trainExample))
return trainExamples
paddedLength = 100
trainExamples = list(buildExamples(trainText[:-1], paddedLength))
trainLabels = [ord(token) for token in trainText[:-1]]
testExamples = list(buildExamples(testText[:-1], paddedLength))
testLabels = [ord(token) for token in testText[1:]]
trainDataset = tf.data.Dataset.from_tensor_slices((trainExamples, trainLabels))
testDataset = tf.data.Dataset.from_tensor_slices((testExamples, testLabels))
model = tf.keras.Sequential([
tf.keras.layers.Flatten(input_shape=(100,)),
tf.keras.layers.Dense(128, activation="relu"),
tf.keras.layers.Dense(128)
])
I am attempting to construct a simple text generative model. Obviously, an RNN or transformer network would be more efficient but I'm not that skilled. On this sequential model, I receive the following error. Input 0 of layer "dense_22" is incompatible with the layer: expected axis -1 of input shape to have value 100, but received input with shape (100, 1) Unfortunately, I am unsure as what to do.
I have tried adjusting the input shape to no avail.
