Image caption generator
For this code:
train the model
dump(tokenizer, open('/content/tokenizer.pkl', 'wb'))
model = define_model(vocab_size, max_length)
train the model, run epochs manually and save after each epoch
epochs = 20
steps = len(train_descriptions)
for i in range(epochs):
# create the data generator
generator = data_generator(train_descriptions, train_features, tokenizer, max_length)
# fit for one epoch
model.fit_generator(generator, epochs=1, steps_per_epoch=steps, verbose=1)
# save model
model.save('model_' + str(i) + '.h5')
This is error
ValueError Traceback (most recent call last)
in ()
9 generator = data_generator(train_descriptions, train_features, tokenizer, max_length)
10 # fit for one epoch
---> 11 model.fit_generator(generator, epochs=1, steps_per_epoch=steps, verbose=1)
12 # save model
13 model.save('model_' + str(i) + '.h5')
12 frames
/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/func_graph.py in wrapper(*args, **kwargs)
971 except Exception as e: # pylint:disable=broad-except
972 if hasattr(e, "ag_error_metadata"):
--> 973 raise e.ag_error_metadata.to_exception(e)
974 else:
975 raise
ValueError: in user code:
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py:806 train_function *
return step_function(self, iterator)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py:796 step_function **
outputs = model.distribute_strategy.run(run_step, args=(data,))
/usr/local/lib/python3.6/dist-packages/tensorflow/python/distribute/distribute_lib.py:1211 run
return self._extended.call_for_each_replica(fn, args=args, kwargs=kwargs)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/distribute/distribute_lib.py:2585 call_for_each_replica
return self._call_for_each_replica(fn, args, kwargs)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/distribute/distribute_lib.py:2945 _call_for_each_replica
return fn(*args, **kwargs)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py:789 run_step **
outputs = model.train_step(data)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py:757 train_step
self.trainable_variables)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py:2737 _minimize
trainable_variables))
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/optimizer_v2/optimizer_v2.py:562 _aggregate_gradients
filtered_grads_and_vars = _filter_grads(grads_and_vars)
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/optimizer_v2/optimizer_v2.py:1271 _filter_grads
([v.name for _, v in grads_and_vars],))
ValueError: No gradients provided for any variable: ['embedding_3/embeddings:0', 'dense_9/kernel:0', 'dense_9/bias:0', 'lstm_3/lstm_cell_3/kernel:0', 'lstm_3/lstm_cell_3/recurrent_kernel:0', 'lstm_3/lstm_cell_3/bias:0', 'dense_10/kernel:0', 'dense_10/bias:0', 'dense_11/kernel:0', 'dense_11/bias:0'].
Image caption generator
For this code:
train the model
dump(tokenizer, open('/content/tokenizer.pkl', 'wb'))
model = define_model(vocab_size, max_length)
train the model, run epochs manually and save after each epoch
epochs = 20
steps = len(train_descriptions)
for i in range(epochs):
# create the data generator
generator = data_generator(train_descriptions, train_features, tokenizer, max_length)
# fit for one epoch
model.fit_generator(generator, epochs=1, steps_per_epoch=steps, verbose=1)
# save model
model.save('model_' + str(i) + '.h5')
This is error
ValueError Traceback (most recent call last)
in ()
9 generator = data_generator(train_descriptions, train_features, tokenizer, max_length)
10 # fit for one epoch
---> 11 model.fit_generator(generator, epochs=1, steps_per_epoch=steps, verbose=1)
12 # save model
13 model.save('model_' + str(i) + '.h5')
12 frames
/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/func_graph.py in wrapper(*args, **kwargs)
971 except Exception as e: # pylint:disable=broad-except
972 if hasattr(e, "ag_error_metadata"):
--> 973 raise e.ag_error_metadata.to_exception(e)
974 else:
975 raise
ValueError: in user code: