My trainX is (3350, 1, 8) and my TrainY is (3350, 2). I am getting some errors, but I don't understand what's the problem. I am getting this error when tuning the hyperparameters using the LSTM layer.
Output exceeds the size limit. Open the full output data in a text editor
--------------------------------------------------------------------------- InvalidArgumentError Traceback (most recent call last) /tmp/ipykernel_4525/3330873115.py in <module>
----> 1 bayesian_opt_tuner.search(trainX, trainY,epochs=100,
2 #validation_data=(X_test, y_test)
3 validation_split=0.2,verbose=1)
~/anaconda3/lib/python3.9/site-packages/keras_tuner/engine/base_tuner.py in search(self, *fit_args, **fit_kwargs)
181
182 self.on_trial_begin(trial)
--> 183 results = self.run_trial(trial, *fit_args, **fit_kwargs)
184 # `results` is None indicates user updated oracle in `run_trial()`.
185 if result is None:
~/anaconda3/lib/python3.9/site-packages/keras_tuner/engine/tuner.py in run_trial(self, trial, *args, **kwargs)
293 callbacks.append(model_checkpoint)
294 copied_kwargs["callbacks"] = callbacks
--> 295 obj_value = self._build_and_fit_model(trial, *args, **copied_kwargs)
296
297 histories.append(obj_value)
~/anaconda3/lib/python3.9/site-packages/keras_tuner/engine/tuner.py in
_build_and_fit_model(self, trial, *args, **kwargs)
220 hp = trial.hyperparameters
221 model = self._try_build(hp)
--> 222 results = self.hypermodel.fit(hp, model, *args, **kwargs) ...
File "/home/vareeshadki/anaconda3/lib/python3.9/site-packages/keras/backend.py", line 5238, in sparse_categorical_crossentropy
res = cf.nn.sparse_softmax_cross_entropy_with_logits( Node: 'sparse_categorical_crossentropy/SparseSoftmaxCrossEntropyWithLogits/SparseSoftmaxCrossEntropyWithLogits' logits and labels must have the same first dimension, got logits shape [32,2] and labels shape [64] [[{{node sparse_categorical_crossentropy/SparseSoftmaxCrossEntropyWithLogits/SparseSoftmaxCrossEntropyWithLogits}}]] [Op:__inference_train_function_1763748]
My code:
import os
from kerastuner.tuners import BayesianOptimization
def build_model(hp):
model = tf.keras.Sequential()
hp_units = hp.Int('units', min_value=32, max_value=512, step=10)
model.add(LSTM(hp_units,activation='relu'))
model.add(Dense(2))
hp_learning_rate = hp.Choice('learning_rate', values=[1e-2, 1e-3, 1e-4])
model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=hp_learning_rate),
loss=tf.keras.losses.SparseCategoricalCrossentropy(),
metrics=['accuracy'])
return model
bayesian_opt_tuner = BayesianOptimization(
build_model,
objective='mse',
max_trials=3,
executions_per_trial=2,
directory=os.path.normpath('C:/keras_tuning'),
project_name='kerastuner_bayesian_poc',
overwrite=True)
bayesian_opt_tuner.search(trainX, trainY,epochs=100,
#validation_data=(X_test, y_test)
validation_split=0.2,verbose=1)
