CODEWITHSUNDEEP
pythontensorflowmachine-learningkerasdeep-learning
Taisia Sharapova
Taisia Sharapova

Reputation: 19

Keras: Trying to model.predict() gives "ValueError: Tensor's shape is not compatible with supplied shape"

I'm following the TensorFlow Keras tutorial for text generation. The training part works perfectly, but when I try to predict the next token, I get an error. Here's all the important code:

  1. Making the vocabulary and dataset.
vocab = sorted(set(text))

char2index = { c:i for i, c in enumerate(vocab) }
index2char = np.array(vocab)
chars_to_int = np.array([char2index[c] for c in text])

char_dataset = tf.data.Dataset.from_tensor_slices(chars_to_int)
sequences = char_dataset.batch(seq_length + 1, drop_remainder=True)

def split_input_and_target(sequence):
    input_ = sequence[:-1]
    target_ = sequence[1:]
    return input_, target_

dataset = sequences.map(split_input_and_target)
dataset = dataset.shuffle(BUFFER_SIZE).batch(BATCH_SIZE, drop_remainder=True)
  1. Building the model (important part here is that BATCH_SIZE = 64):
model = tf.keras.Sequential()

model.add(tf.keras.layers.Embedding(len(vocab), EMBEDDING_DIM,
                    batch_input_shape=[BATCH_SIZE, None]))
# here are a few more layers

model.compile(loss="sparse_categorical_crossentropy", optimizer="adam")
model.fit(dataset, epochs=EPOCHS)
  1. Actually trying to generate text (this one was copied almost directly from the tutorial after I started getting desperate):
num_tokens = 100
seed = "some text"
input_eval = [char2index[c] for c in seed]
input_eval = tf.expand_dims(input_eval, 0)
text_generated = []

model.reset_states()

for i in range(num_tokens):
    predictions = model(input_eval)
    predictions = tf.squeeze(predictions, 0)
    # more stuff

Then, I first get a warning:

WARNING:tensorflow:Model was constructed with shape (64, None) for input Tensor("embedding_14_input:0", shape=(64, None), dtype=float32), but it was called on an input with incompatible shape (1, 9).

Then it gives me an error:

---->3     predictions = model(input_eval)
...
ValueError: Tensor's shape (9, 64, 256) is not compatible with supplied shape [9, 1, 256]

The second number, 64, is my batch size. If I change BATCH_SIZE to 1, everything works and all is fine, but this is obviously not the solution I am hoping for.

Upvotes: 0

Views: 2587

Answers (1)

Taisia Sharapova
Taisia Sharapova

Reputation: 19

(I somehow managed to miss a step in the tutorial despite reading it several times over the past few hours.)

Here's the relevant passage:

To keep this prediction step simple, use a batch size of 1.

Because of the way the RNN state is passed from timestep to timestep, the model only accepts a fixed batch size once built.

To run the model with a different batch_size, we need to rebuild the model and restore the weights from the checkpoint.

tf.train.latest_checkpoint(checkpoint_dir)

model = build_model(vocab_size, embedding_dim, rnn_units, batch_size=1)
model.load_weights(tf.train.latest_checkpoint(checkpoint_dir))
model.build(tf.TensorShape([1, None]))

I hope my silly mistake will help somebody to remember to reload the model in the future!

Upvotes: 1

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