I want to save the best checkpoint when my model is training, but the callback does not work as I expect. According to Saving best model in Keras this code should work.
model = Sequential()
model.add(Conv1D(filters=32, kernel_size=8, input_shape=(X_train.shape[1], 4)))
model.add(MaxPooling1D(pool_size=4))
model.add(Flatten())
model.add(Dense(16, activation='relu'))
model.add(Dense(2, activation='softmax'))
model.compile(loss='binary_crossentropy', optimizer='adam',
metrics=['accuracy'])
model.summary()
stop = EarlyStopping(monitor='val_loss', patience=15, verbose=1, mode='min')
save = ModelCheckpoint('./my_model.hdf5', save_best_only=True, monitor='val_loss', mode='min')
reduce_lr = ReduceLROnPlateau(monitor='val_loss', factor=0.1, patience=10, verbose=1, epsilon=1e-4, mode='min')
history = model.fit(X_train, y_train, epochs=25, verbose=0, callbacks=[stop, save, reduce_lr], validation_split=0.25)
However it keeps giving me following error:
AttributeError Traceback (most recent call last)
<ipython-input-28-f86f439eae5a> in <module>()
17 reduce_lr_loss = ReduceLROnPlateau(monitor='val_loss', factor=0.1, patience=7, verbose=1, epsilon=1e-4, mode='min')
18
---> 19 history = model.fit(X_train, y_train, batch_size=batch_size, epochs=50, verbose=0, callbacks=[earlyStopping, mcp_save, reduce_lr_loss], validation_split=0.25)
20
21
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py in fit(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, max_queue_size, workers, use_multiprocessing, **kwargs)
878 initial_epoch=initial_epoch,
879 steps_per_epoch=steps_per_epoch,
--> 880 validation_steps=validation_steps)
881
882 def evaluate(self,
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training_arrays.py in model_iteration(model, inputs, targets, sample_weights, batch_size, epochs, verbose, callbacks, val_inputs, val_targets, val_sample_weights, shuffle, initial_epoch, steps_per_epoch, validation_steps, mode, validation_in_fit, **kwargs)
323 # Callbacks batch_begin.
324 batch_logs = {'batch': batch_index, 'size': len(batch_ids)}
--> 325 callbacks._call_batch_hook(mode, 'begin', batch_index, batch_logs)
326 progbar.on_batch_begin(batch_index, batch_logs)
327
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/callbacks.py in _call_batch_hook(self, mode, hook, batch, logs)
194 t_before_callbacks = time.time()
195 for callback in self.callbacks:
--> 196 batch_hook = getattr(callback, hook_name)
197 batch_hook(batch, logs)
198 self._delta_ts[hook_name].append(time.time() - t_before_callbacks)
AttributeError: 'EarlyStopping' object has no attribute 'on_train_batch_begin'
I have successfully used this code for my functional model, but I am not sure what the problem is here with the sequential model.
From the stack trace, I notice that you're using tensorflow.keras but EarlyStopping from keras (based on the the other answer you referenced). This is the cause of the error.
This should work(import from tensorflow keras):
from tensorflow.keras.callbacks import EarlyStopping
If you want to use all of Keras's functionality you can't use Tensorflow 2.0. Keras integration is incomplete.
pip install --upgrade "tensorflow==1.4" "keras>=2.0"
Related
I use word2vec and biLstm to implement sentiment analysis for movie reviews. When I train my model on Jupyter notebook, I always get the TypeError: update() got an unexpected keyword argument 'force' at the last batch of the first epoch.
here is my code:
batch_size = 50
result = model.fit(
X_train,
Y_train,
validation_data=(X_test, Y_test),
batch_size=batch_size,
epochs=5
)
and the error:
Train on 1600 samples, validate on 400 samples
Epoch 1/5
1550/1600 [============================>.] - ETA: 2s - loss: 0.7257 - acc: 0.4961
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-360-8eaf5ac2ad33> in <module>()
17 validation_data=(X_test, Y_test),
18 batch_size=batch_size,
---> 19 epochs=5
20 )
~/opt/anaconda3/envs/WDPS/lib/python3.6/site-packages/keras/models.py in fit(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, **kwargs)
958 initial_epoch=initial_epoch,
959 steps_per_epoch=steps_per_epoch,
--> 960 validation_steps=validation_steps)
961
962 def evaluate(self, x, y, batch_size=32, verbose=1,
~/opt/anaconda3/envs/WDPS/lib/python3.6/site-packages/keras/engine/training.py in fit(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, **kwargs)
1648 initial_epoch=initial_epoch,
1649 steps_per_epoch=steps_per_epoch,
-> 1650 validation_steps=validation_steps)
1651
1652 def evaluate(self, x=None, y=None,
~/opt/anaconda3/envs/WDPS/lib/python3.6/site-packages/keras/engine/training.py in _fit_loop(self, f, ins, out_labels, batch_size, epochs, verbose, callbacks, val_f, val_ins, shuffle, callback_metrics, initial_epoch, steps_per_epoch, validation_steps)
1231 for l, o in zip(out_labels, val_outs):
1232 epoch_logs['val_' + l] = o
-> 1233 callbacks.on_epoch_end(epoch, epoch_logs)
1234 if callback_model.stop_training:
1235 break
~/opt/anaconda3/envs/WDPS/lib/python3.6/site-packages/keras/callbacks.py in on_epoch_end(self, epoch, logs)
71 logs = logs or {}
72 for callback in self.callbacks:
---> 73 callback.on_epoch_end(epoch, logs)
74
75 def on_batch_begin(self, batch, logs=None):
~/opt/anaconda3/envs/WDPS/lib/python3.6/site-packages/keras/callbacks.py in on_epoch_end(self, epoch, logs)
304 self.log_values.append((k, logs[k]))
305 if self.verbose:
--> 306 self.progbar.update(self.seen, self.log_values, force=True)
307
308
TypeError: update() got an unexpected keyword argument 'force'
At first, I have a line of code verbose = 1 under the epochs=5. There will be the same error and the arrow point to verbose = 1. Then I change it to verbose=2 or just delete verbose. But I still have the problem.
I tried to change the batch size and the amount of training set. but it still didn't work out.
It always showed at the last batch.
python version= 3.6.2
keras version = 2.1.1
I am building a model using tensorflow. I trained my model and it worked normally. Then, I modified my code and when I try to train my model, I am getting a AlreadyExistError. I restart my Jupyter Notebook but I’m still getting the same error. I need some help please.
here is my piece of code where I build the network and train it. The problem occurs in the last line.
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Conv1D, Dropout, Dense, Flatten, LSTM, MaxPooling1D, Bidirectional
from tensorflow.keras.optimizers import Adam
from keras.callbacks import EarlyStopping, TensorBoard
model = Sequential()
model.add(Conv1D(32, kernel_size=3, activation='elu', padding='same',
input_shape=(vector_size, 1)))
model.add(Conv1D(32, kernel_size=3, activation='elu', padding='same'))
model.add(Conv1D(32, kernel_size=3, activation='relu', padding='same'))
model.add(MaxPooling1D(pool_size=3))
model.add(Bidirectional(LSTM(512, dropout=0.2, recurrent_dropout=0.3)))
model.add(Dense(512, activation='sigmoid'))
model.add(Dropout(0.2))
model.add(Dense(512, activation='sigmoid'))
model.add(Dropout(0.25))
model.add(Dense(512, activation='sigmoid'))
model.add(Dropout(0.25))
model.add(Dense(2, activation='softmax'))
model.compile(loss='categorical_crossentropy', optimizer=Adam(lr=0.0001, decay=1e-6), metrics=['accuracy'])
tensorboard = TensorBoard(log_dir='logs/', histogram_freq=0, write_graph=True, write_images=True)
model.summary()
model.fit(np.array(x_train), np.array(y_train), batch_size=batch_size, epochs=no_epochs,
validation_data=(np.array(x_test), np.array(y_test)), callbacks=[tensorboard, EarlyStopping(min_delta=0.0001, patience=3)])
Train on 90000 samples, validate on 10000 samples Epoch 1/10
500/90000 [..............................] - ETA: 2:00:49
/anaconda3/lib/python3.7/site-packages/keras/callbacks/callbacks.py:846:
RuntimeWarning: Early stopping conditioned on metric val_loss which
is not available. Available metrics are: (self.monitor,
','.join(list(logs.keys()))), RuntimeWarning
--------------------------------------------------------------------------- AlreadyExistsError Traceback (most recent call
last) in
1 model.fit(np.array(x_train), np.array(y_train), batch_size=batch_size, epochs=no_epochs,
----> 2 validation_data=(np.array(x_test), np.array(y_test)), callbacks=[tensorboard, EarlyStopping(min_delta=0.0001, patience=3)])
3 print('You can continue')
/anaconda3/lib/python3.7/site-packages/tensorflow_core/python/keras/engine/training.py
in fit(self, x, y, batch_size, epochs, verbose, callbacks,
validation_split, validation_data, shuffle, class_weight,
sample_weight, initial_epoch, steps_per_epoch, validation_steps,
validation_freq, max_queue_size, workers, use_multiprocessing,
**kwargs)
817 max_queue_size=max_queue_size,
818 workers=workers,
--> 819 use_multiprocessing=use_multiprocessing)
820
821 def evaluate(self,
/anaconda3/lib/python3.7/site-packages/tensorflow_core/python/keras/engine/training_v2.py
in fit(self, model, x, y, batch_size, epochs, verbose, callbacks,
validation_split, validation_data, shuffle, class_weight,
sample_weight, initial_epoch, steps_per_epoch, validation_steps,
validation_freq, max_queue_size, workers, use_multiprocessing,
**kwargs)
340 mode=ModeKeys.TRAIN,
341 training_context=training_context,
--> 342 total_epochs=epochs)
343 cbks.make_logs(model, epoch_logs, training_result, ModeKeys.TRAIN)
344
/anaconda3/lib/python3.7/site-packages/tensorflow_core/python/keras/engine/training_v2.py
in run_one_epoch(model, iterator, execution_function, dataset_size,
batch_size, strategy, steps_per_epoch, num_samples, mode,
training_context, total_epochs)
126 step=step, mode=mode, size=current_batch_size) as batch_logs:
127 try:
--> 128 batch_outs = execution_function(iterator)
129 except (StopIteration, errors.OutOfRangeError):
130 # TODO(kaftan): File bug about tf function and errors.OutOfRangeError?
/anaconda3/lib/python3.7/site-packages/tensorflow_core/python/keras/engine/training_v2_utils.py
in execution_function(input_fn)
96 # numpy translates Tensors to values in Eager mode.
97 return nest.map_structure(_non_none_constant_value,
---> 98 distributed_function(input_fn))
99
100 return execution_function
/anaconda3/lib/python3.7/site-packages/tensorflow_core/python/eager/def_function.py
in call(self, *args, **kwds)
566 xla_context.Exit()
567 else:
--> 568 result = self._call(*args, **kwds)
569
570 if tracing_count == self._get_tracing_count():
/anaconda3/lib/python3.7/site-packages/tensorflow_core/python/eager/def_function.py
in _call(self, *args, **kwds)
630 # Lifting succeeded, so variables are initialized and we can run the
631 # stateless function.
--> 632 return self._stateless_fn(*args, **kwds)
633 else:
634 canon_args, canon_kwds = \
/anaconda3/lib/python3.7/site-packages/tensorflow_core/python/eager/function.py
in call(self, *args, **kwargs) 2361 with self._lock:
2362 graph_function, args, kwargs =
self._maybe_define_function(args, kwargs)
-> 2363 return graph_function._filtered_call(args, kwargs) # pylint: disable=protected-access 2364 2365 #property
/anaconda3/lib/python3.7/site-packages/tensorflow_core/python/eager/function.py
in _filtered_call(self, args, kwargs) 1609 if
isinstance(t, (ops.Tensor, 1610
resource_variable_ops.BaseResourceVariable))),
-> 1611 self.captured_inputs) 1612 1613 def _call_flat(self, args, captured_inputs, cancellation_manager=None):
/anaconda3/lib/python3.7/site-packages/tensorflow_core/python/eager/function.py
in _call_flat(self, args, captured_inputs, cancellation_manager)
1690 # No tape is watching; skip to running the function.
1691 return
self._build_call_outputs(self._inference_function.call(
-> 1692 ctx, args, cancellation_manager=cancellation_manager)) 1693
forward_backward = self._select_forward_and_backward_functions(
1694 args,
/anaconda3/lib/python3.7/site-packages/tensorflow_core/python/eager/function.py
in call(self, ctx, args, cancellation_manager)
543 inputs=args,
544 attrs=("executor_type", executor_type, "config_proto", config),
--> 545 ctx=ctx)
546 else:
547 outputs = execute.execute_with_cancellation(
/anaconda3/lib/python3.7/site-packages/tensorflow_core/python/eager/execute.py
in quick_execute(op_name, num_outputs, inputs, attrs, ctx, name)
65 else:
66 message = e.message
---> 67 six.raise_from(core._status_to_exception(e.code, message), None)
68 except TypeError as e:
69 keras_symbolic_tensors = [
/anaconda3/lib/python3.7/site-packages/six.py in raise_from(value,
from_value)
AlreadyExistsError: Resource
__per_step_0/sequential/bidirectional/forward_lstm/while_grad/body/_429/gradients/AddN_13/tmp_var/N10tensorflow19TemporaryVariableOp6TmpVarE
[[{{node
sequential/bidirectional/forward_lstm/while_grad/body/_429/gradients/AddN_13/tmp_var}}]]
[Op:__inference_distributed_function_12060]
Function call stack: distributed_function
You must have a problem on same architectures from previous run being seen while training.
This should reset the keras session :
from tensorflow.keras import backend
backend.clear_session()
In my case I got the problem when using EMR on AWS, and I solved just uninstalling Keras, if you have tensorflow you already have Keras, but there are other packages that depends of the old Keras library. So you need to uninstall the old Keras to avoid this problem.
!pip uninstall keras
I am trying to train a Deep Neural Network using MNIST data set.
BATCH_SIZE = 100
train_data = train_data.batch(BATCH_SIZE)
validation_data = validation_data.batch(num_validation_samples)
test_data = scaled_test_data.batch(num_test_samples)
validation_inputs, validation_targets = next(iter(validation_data))
input_size = 784
output_size = 10
hidden_layer_size = 50
model = tf.keras.Sequential([
tf.keras.layers.Flatten(input_shape=(28,28,1)),
tf.keras.layers.Dense(hidden_layer_size, activation='relu'),
tf.keras.layers.Dense(hidden_layer_size, activation='relu'),
tf.keras.layers.Dense(output_size, activation='softmax')
])
model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
NUM_EPOCHS = 5
model.fit(train_data, epochs=NUM_EPOCHS, validation_data=(validation_inputs,validation_targets))
The model.fit is throwing the following error
-------------------------------------------------------------------------
--
ValueError Traceback (most recent call last)
<ipython-input-58-c083185dafc6> in <module>
1 NUM_EPOCHS = 5
----> 2 model.fit(train_data, epochs=NUM_EPOCHS, validation_data=(validation_inputs,validation_targets))
~/anaconda3/envs/py3-TF2/lib/python3.7/site-packages/tensorflow_core/python/keras/engine/training.py in fit(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_freq, max_queue_size, workers, use_multiprocessing, **kwargs)
726 max_queue_size=max_queue_size,
727 workers=workers,
--> 728 use_multiprocessing=use_multiprocessing)
729
730 def evaluate(self,
~/anaconda3/envs/py3-TF2/lib/python3.7/site-packages/tensorflow_core/python/keras/engine/training_v2.py in fit(self, model, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_freq, **kwargs)
222 validation_data=validation_data,
223 validation_steps=validation_steps,
--> 224 distribution_strategy=strategy)
225
226 total_samples = _get_total_number_of_samples(training_data_adapter)
~/anaconda3/envs/py3-TF2/lib/python3.7/site-packages/tensorflow_core/python/keras/engine/training_v2.py in _process_training_inputs(model, x, y, batch_size, epochs, sample_weights, class_weights, steps_per_epoch, validation_split, validation_data, validation_steps, shuffle, distribution_strategy, max_queue_size, workers, use_multiprocessing)
562 class_weights=class_weights,
563 steps=validation_steps,
--> 564 distribution_strategy=distribution_strategy)
565 elif validation_steps:
566 raise ValueError('`validation_steps` should not be specified if '
~/anaconda3/envs/py3-TF2/lib/python3.7/site-packages/tensorflow_core/python/keras/engine/training_v2.py in _process_inputs(model, x, y, batch_size, epochs, sample_weights, class_weights, shuffle, steps, distribution_strategy, max_queue_size, workers, use_multiprocessing)
604 max_queue_size=max_queue_size,
605 workers=workers,
--> 606 use_multiprocessing=use_multiprocessing)
607 # As a fallback for the data type that does not work with
608 # _standardize_user_data, use the _prepare_model_with_inputs.
~/anaconda3/envs/py3-TF2/lib/python3.7/site-packages/tensorflow_core/python/keras/engine/data_adapter.py in __init__(self, x, y, sample_weights, batch_size, epochs, steps, shuffle, **kwargs)
252 if not batch_size:
253 raise ValueError(
--> 254 "`batch_size` or `steps` is required for `Tensor` or `NumPy`"
255 " input data.")
256
ValueError: `batch_size` or `steps` is required for `Tensor` or `NumPy` input data.
The training and validation data are obtained from MNIST dataset. Some part of the data are taken as training data and some as testing data.
What am I doing wrong here?
Update
As per Dominques suggestion, I have changed model.fit to
model.fit(train_data, batch_size=128, epochs=NUM_EPOCHS, validation_data=(validation_inputs,validation_targets))
But now, I get the following error
ValueError: The `batch_size` argument must not be specified for the given input type. Received input: <BatchDataset shapes: ((None, 28, 28, 1), (None,)), types: (tf.float32, tf.int64)>, batch_size: 128
The tf doc will give you more clues why you get the error.
https://www.tensorflow.org/api_docs/python/tf/keras/Model#fit
validation_data: Data on which to evaluate the loss and any model metrics at the end of each epoch. The model will not be trained on this data. validation_data will override validation_split. validation_data could be:
• tuple (x_val, y_val) of Numpy arrays or tensors
• tuple (x_val, y_val, val_sample_weights) of Numpy arrays
• dataset
For the first two cases, batch_size must be provided. For the last case, validation_steps must be provided.
Since You already have the validation dataset batched, consider to use it directly and specify validation steps as below.
BATCH_SIZE = 100
train_data = train_data.batch(BATCH_SIZE)
validation_data = validation_data.batch(BATCH_SIZE)
...
model.fit(train_data, epochs=NUM_EPOCHS, validation_data=validation_data,validation_steps=1)
You need to specify the batch size, i.e. how many data points should be included in each iteration. If you look at the documentation you will see that there is no default value set.
https://www.tensorflow.org/api_docs/python/tf/keras/Sequential
you can set the value by adding batch_size to the fit command. Good values are normally numbers along the line of 2**n, as this allows for more efficient processing with multiple cores. For you this shouldn't make a strong difference though :)
model.fit(train_data,
batch_size=128
epochs=NUM_EPOCHS,
validation_data=(validation_inputs,validation_targets))
Why nobody mention i don't know but your problem is Y_train data. You don't supply it as an argument to your model..
model.fit(X_Train, y_train, batch_size=None, epochs=1, verbose=1, callbacks=None, validation_split=0.0, validation_data=None, shuffle=True, class_weight=None, sample_weight=None, initial_epoch=0, steps_per_epoch=None, validation_steps=None, validation_freq=1, max_queue_size=10, workers=1, use_multiprocessing=False)
Instead of y_train you are giving :
model.fit(train_data, batch_size=128 ....
And getting an Error saying :
ValueError: `batch_size` or `steps` is required for `Tensor` or `NumPy` input data.
I hope it helps.
model.fit(train_data, epochs=NUM_EPOCHS, validation_data=(validation_inputs, validation_targets), verbose=2)
change to (by adding validation_steps=1) will do the trick
model.fit(train_data, epochs=NUM_EPOCHS, validation_data=(validation_inputs, validation_targets),validation_steps=1, verbose=2)
I changed the input_shape=(28,28,1) to input_shape=(28,28,3) and it worked for me.
I have a Dask DataFrame that I want to use for fitting a Keras autoencoder model:
DataFrame:
import dask.dataframe as dd
input_df = dd.read_csv(file_path)
input_df.dtypes
_2 float64
_3 float64
_4 float64
_5 float64 ...
Keras model:
autoencoder = Sequential()
autoencoder.add(Dense(dense[0], input_shape=(dense[0],), activation = 'relu' ))
autoencoder.add(Dense(dense[1], activation = 'relu' ))
autoencoder.add(Dense(dense[2], activation = 'relu' ))
autoencoder.add(Dense(dense[3], activation = 'relu' ))
autoencoder.add(Dense(dense[0], activation = 'relu' ))
autoencoder.compile(loss='mse',
optimizer='adam',
metrics=['mse'])
When I pass the DataFrame for fitting:
autoencoder.fit(input_df, input_df,
batch_size=batch_size,
epochs=epochs,
verbose=1,
validation_split = val_split)
I get the error:
TypeError Traceback (most recent call last)
<ipython-input-23-d0480d8a460d> in <module>()
3 epochs=epochs,
4 verbose=1,
----> 5 validation_split = val_split)
~/anaconda3/envs/py36/lib/python3.6/site-packages/keras/engine/training.py in fit(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, **kwargs)
950 sample_weight=sample_weight,
951 class_weight=class_weight,
--> 952 batch_size=batch_size)
953 # Prepare validation data.
954 do_validation = False
~/anaconda3/envs/py36/lib/python3.6/site-packages/keras/engine/training.py in _standardize_user_data(self, x, y, sample_weight, class_weight, check_array_lengths, batch_size)
799 for (ref, sw, cw, mode) in
800 zip(y, sample_weights, class_weights,
--> 801 feed_sample_weight_modes)
802 ]
803 # Check that all arrays have the same length.
~/anaconda3/envs/py36/lib/python3.6/site-packages/keras/engine/training.py in <listcomp>(.0)
797 sample_weights = [
798 standardize_weights(ref, sw, cw, mode)
--> 799 for (ref, sw, cw, mode) in
800 zip(y, sample_weights, class_weights,
801 feed_sample_weight_modes)
~/anaconda3/envs/py36/lib/python3.6/site-packages/keras/engine/training_utils.py in standardize_weights(y, sample_weight, class_weight, sample_weight_mode)
522 else:
523 if sample_weight_mode is None:
--> 524 return np.ones((y.shape[0],), dtype=K.floatx())
525 else:
526 return np.ones((y.shape[0], y.shape[1]), dtype=K.floatx())
~/anaconda3/envs/py36/lib/python3.6/site-packages/numpy/core/numeric.py in ones(shape, dtype, order)
201
202 """
--> 203 a = empty(shape, dtype, order)
204 multiarray.copyto(a, 1, casting='unsafe')
205 return a
TypeError: 'float' object cannot be interpreted as an integer
Would appreciate some help! Thanks!
I am using tensorflow==1.2.1 and Keras==2.0.6 to build a model:
input_num = X_norm_keras[:,2:].shape[1]
model_keras = Sequential()
model_keras.add(Dense(10, input_dim=input_num, activation='relu'))
model_keras.add(Dense(1, activation='linear'))
kernel_regularizer=regularizers.l2(0.2), optimizer='adam')
model_keras.compile(loss='mean_squared_error', kernel_regularizer=regularizers.l2(0.2), optimizer='adam')
model_keras.fit(X_norm_train[:,2:], y_norm_train, batch_size=25, epochs=250)
But got the following errors:
Using TensorFlow backend.
total data points = (25, 106)
Epoch 1/250
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-4-4cbd897903e7> in <module>()
102 model_keras.compile(loss='mean_squared_error', kernel_regularizer=regularizers.l2(0.2), optimizer='adam')
--> 103 model_keras.fit(X_norm_train[:,2:], y_norm_train, batch_size=25, epochs=250)
/usr/local/lib/python3.4/dist-packages/keras/models.py in fit(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, **kwargs)
861 class_weight=class_weight,
862 sample_weight=sample_weight,
--> 863 initial_epoch=initial_epoch)
864
865 def evaluate(self, x, y, batch_size=32, verbose=1,
/usr/local/lib/python3.4/dist-packages/keras/engine/training.py in fit(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, **kwargs)
1428 val_f=val_f, val_ins=val_ins, shuffle=shuffle,
1429 callback_metrics=callback_metrics,
-> 1430 initial_epoch=initial_epoch)
1431
1432 def evaluate(self, x, y, batch_size=32, verbose=1, sample_weight=None):
/usr/local/lib/python3.4/dist-packages/keras/engine/training.py in _fit_loop(self, f, ins, out_labels, batch_size, epochs, verbose, callbacks, val_f, val_ins, shuffle, callback_metrics, initial_epoch)
1077 batch_logs['size'] = len(batch_ids)
1078 callbacks.on_batch_begin(batch_index, batch_logs)
-> 1079 outs = f(ins_batch)
1080 if not isinstance(outs, list):
1081 outs = [outs]
/usr/local/lib/python3.4/dist-packages/keras/backend/tensorflow_backend.py in __call__(self, inputs)
2266 updated = session.run(self.outputs + [self.updates_op],
2267 feed_dict=feed_dict,
-> 2268 **self.session_kwargs)
2269 return updated[:len(self.outputs)]
2270
TypeError: run() got an unexpected keyword argument 'kernel_regularizer'
Am I missing anything here? Thanks!
The regularizer kernel_regularizer=regularizers.l2(0.2) should be an argument of Dense(), not model.compile().
From the documentation of model.compile():
**kwargs: When using the Theano/CNTK backends, these arguments are passed into K.function. When using the TensorFlow backend, these arguments are passed into tf.Session.run.
That's why you are seeing an error coming from run().