Related
I want to run my code on GPU provided by Kaggle. I am able to run my code on CPU though but unable to migrate it properly to run on Kaggle GPU I guess.
On running this
with tf.device("/device:GPU:0"):
hist = model.fit(x=X_train, y=Y_train, validation_data=(X_test, Y_test), batch_size=25, epochs=20, callbacks=callbacks_list)
and getting this error
---------------------------------------------------------------------------
RuntimeError Traceback (most recent call last)
<ipython-input-28-cdb8b009cd85> in <module>
1 with tf.device("/device:GPU:0"):
----> 2 hist = model.fit(x=X_train, y=Y_train, validation_data=(X_test, Y_test), batch_size=25, epochs=20, callbacks=callbacks_list)
/opt/conda/lib/python3.6/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 self._assert_compile_was_called()
818 self._check_call_args('evaluate')
--> 819
820 func = self._select_training_loop(x)
821 return func.evaluate(
/opt/conda/lib/python3.6/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)
233
234 recreate_training_iterator = (
--> 235 training_data_adapter.should_recreate_iterator(steps_per_epoch))
236 if not steps_per_epoch:
237 # TODO(b/139762795): Add step inference for when steps is None to
/opt/conda/lib/python3.6/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)
591 class_weights=None,
592 shuffle=False,
--> 593 steps=None,
594 distribution_strategy=None,
595 max_queue_size=10,
/opt/conda/lib/python3.6/site-packages/tensorflow_core/python/keras/engine/training_v2.py in _process_inputs(model, mode, x, y, batch_size, epochs, sample_weights, class_weights, shuffle, steps, distribution_strategy, max_queue_size, workers, use_multiprocessing)
704 """Provide a scope for running one batch."""
705 batch_logs = {'batch': step, 'size': size}
--> 706 self.callbacks._call_batch_hook(
707 mode, 'begin', step, batch_logs)
708 self.progbar.on_batch_begin(step, batch_logs)
/opt/conda/lib/python3.6/site-packages/tensorflow_core/python/keras/engine/data_adapter.py in __init__(self, x, y, sample_weights, sample_weight_modes, batch_size, epochs, steps, shuffle, **kwargs)
355 sample_weights = _process_numpy_inputs(sample_weights)
356
--> 357 # If sample_weights are not specified for an output use 1.0 as weights.
358 if (sample_weights is not None and
359 any([sw is None for sw in sample_weights])):
/opt/conda/lib/python3.6/site-packages/tensorflow_core/python/keras/engine/data_adapter.py in slice_inputs(self, indices_dataset, inputs)
381 if steps and not batch_size:
382 batch_size = int(math.ceil(num_samples/steps))
--> 383
384 if not batch_size:
385 raise ValueError(
/opt/conda/lib/python3.6/site-packages/tensorflow_core/python/data/ops/dataset_ops.py in from_tensors(tensors)
564 existing iterators.
565
--> 566 Args:
567 unused_dummy: Ignored value.
568
/opt/conda/lib/python3.6/site-packages/tensorflow_core/python/data/ops/dataset_ops.py in __init__(self, element)
2763 init_args: A nested structure representing the arguments to `init_func`.
2764 init_func: A TensorFlow function that will be called on `init_args` each
-> 2765 time a C++ iterator over this dataset is constructed. Returns a nested
2766 structure representing the "state" of the dataset.
2767 next_func: A TensorFlow function that will be called on the result of
/opt/conda/lib/python3.6/site-packages/tensorflow_core/python/data/util/structure.py in normalize_element(element)
111 ops.convert_to_tensor(t, name="component_%d" % i))
112 return nest.pack_sequence_as(element, normalized_components)
--> 113
114
115 def convert_legacy_structure(output_types, output_shapes, output_classes):
/opt/conda/lib/python3.6/site-packages/tensorflow_core/python/framework/ops.py in convert_to_tensor(value, dtype, name, as_ref, preferred_dtype, dtype_hint, ctx, accepted_result_types)
1312 return ret
1313 raise TypeError("%sCannot convert %r with type %s to Tensor: "
-> 1314 "no conversion function registered." %
1315 (_error_prefix(name), value, type(value)))
1316
/opt/conda/lib/python3.6/site-packages/tensorflow_core/python/framework/tensor_conversion_registry.py in _default_conversion_function(***failed resolving arguments***)
50 def _default_conversion_function(value, dtype, name, as_ref):
51 del as_ref # Unused.
---> 52 return constant_op.constant(value, dtype, name=name)
53
54
/opt/conda/lib/python3.6/site-packages/tensorflow_core/python/framework/constant_op.py in constant(value, dtype, shape, name)
256 return _eager_fill(shape.as_list(), t, ctx)
257 raise TypeError("Eager execution of tf.constant with unsupported shape "
--> 258 "(value has %d elements, shape is %s with %d elements)." %
259 (num_t, shape, shape.num_elements()))
260 g = ops.get_default_graph()
/opt/conda/lib/python3.6/site-packages/tensorflow_core/python/framework/constant_op.py in _constant_impl(value, dtype, shape, name, verify_shape, allow_broadcast)
264 value, dtype=dtype, shape=shape, verify_shape=verify_shape,
265 allow_broadcast=allow_broadcast))
--> 266 dtype_value = attr_value_pb2.AttrValue(type=tensor_value.tensor.dtype)
267 const_tensor = g.create_op(
268 "Const", [], [dtype_value.type],
/opt/conda/lib/python3.6/site-packages/tensorflow_core/python/framework/constant_op.py in convert_to_eager_tensor(value, ctx, dtype)
94 dtype = dtypes.as_dtype(dtype).as_datatype_enum
95 ctx.ensure_initialized()
---> 96 return ops.EagerTensor(value, ctx.device_name, dtype)
97
98
RuntimeError: Can't copy Tensor with type string to device /job:localhost/replica:0/task:0/device:GPU:0.
I have also tried installing different tensorflow versions like latest tensorflow, tensorflow-gpu, tensorflow-gpu=1.12, but got no success.
Though I am able to list out CPUs and GPUs by using
from tensorflow.python.client import device_lib
print(device_lib.list_local_devices())
Please help!
I finally got it to work. There was some unknown bug in tensorflow. It is working properly in tf-nightly build.
When I run the model like this, it's worked fine for me.
tf.debugging.set_log_device_placement(True)
try:
with tf.device('/device:XLA_GPU:0'):
X_train = tf.convert_to_tensor(x_train, dtype=tf.int32)
Y_train = tf.convert_to_tensor(y_train, dtype=tf.float32)
X_dev = tf.convert_to_tensor(x_val, dtype=tf.int32)
Y_dev = tf.convert_to_tensor(y_val, dtype=tf.float32)
_model = tf.keras.Model(review_input, preds)
opt = optimizers.Adam()
_model.compile(loss="mean_absolute_error", optimizer=opt, metrics=['acc'])
except RuntimeError as e:
print(e)
history=_model.fit(X_train, Y_train, epochs=100, batch_size=128, validation_data=(X_dev, Y_dev), verbose=1)
I am trying to analyse an Audio file and train the system based on that extracted features but I am getting an error while Fitting the model saying "Tuple Index out of Range". I have provided the shapes of all the arrays I am using in the comment next to the print statement. Could you help me understand how to define the sizes while defining the model.
Please let me know if any more details are required.
import glob
import numpy as np
import pandas as pd
import random
import librosa
import librosa.display
import glob
import os
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelBinarizer
from tensorflow.keras.layers import LSTM, Dense, Dropout, Flatten
from tensorflow.keras.models import Sequential
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint
X, sample_rate = librosa.load(r'C:\Users\Sumanth\Desktop\voice\Speaker-275-3.wav', res_type='kaiser_fast')
print(X.shape) # Shape is (439238,)
#extracting the MFCC feature from Audio signal
mfccs = librosa.feature.mfcc(y=X, sr=sample_rate, n_mfcc=40)
print(mfccs.shape) # Shape is (40, 858)
#manually assigning the label as 275
z = np.asarray(275)
#Validation data
val_x, sample_rate = librosa.load(r'C:\Users\Sumanth\Desktop\voice\Speaker-275-2.wav', res_type='kaiser_fast')
print(val_x.shape) # Shape is (292826,)
val_y=np.asarray(275)
#Building the model
model = Sequential()
model.add(Dense(256, input_shape=(858,),activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(256, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(275,activation='softmax'))
model.compile(loss='categorical_crossentropy', metrics=['accuracy'], optimizer='adam')
#training our model
model.fit(mfccs, z, epochs=5, validation_data=(val_x, val_y))
-------------------ERROR------------------------------------------------------
IndexError Traceback (most recent call last)
<ipython-input-31-adaf98404d0e> in <module>
40 model.compile(loss='categorical_crossentropy', metrics=['accuracy'], optimizer='adam')
41 #training our model
---> 42 model.fit(mfccs, z, epochs=5, validation_data=(val_x, val_y))
43
44
~\AppData\Roaming\Python\Python37\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,
~\AppData\Roaming\Python\Python37\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)
~\AppData\Roaming\Python\Python37\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)
545 max_queue_size=max_queue_size,
546 workers=workers,
--> 547 use_multiprocessing=use_multiprocessing)
548 val_adapter = None
549 if validation_data:
~\AppData\Roaming\Python\Python37\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)
592 batch_size=batch_size,
593 check_steps=False,
--> 594 steps=steps)
595 adapter = adapter_cls(
596 x,
~\AppData\Roaming\Python\Python37\site-packages\tensorflow_core\python\keras\engine\training.py in _standardize_user_data(self, x, y, sample_weight, class_weight, batch_size, check_steps, steps_name, steps, validation_split, shuffle, extract_tensors_from_dataset)
2532 # Check that all arrays have the same length.
2533 if not self._distribution_strategy:
-> 2534 training_utils.check_array_lengths(x, y, sample_weights)
2535 if self._is_graph_network and not self.run_eagerly:
2536 # Additional checks to avoid users mistakenly using improper loss fns.
~\AppData\Roaming\Python\Python37\site-packages\tensorflow_core\python\keras\engine\training_utils.py in check_array_lengths(inputs, targets, weights)
661
662 set_x = set_of_lengths(inputs)
--> 663 set_y = set_of_lengths(targets)
664 set_w = set_of_lengths(weights)
665 if len(set_x) > 1:
~\AppData\Roaming\Python\Python37\site-packages\tensorflow_core\python\keras\engine\training_utils.py in set_of_lengths(x)
656 return set([
657 y.shape[0]
--> 658 for y in x
659 if y is not None and not is_tensor_or_composite_tensor(y)
660 ])
~\AppData\Roaming\Python\Python37\site-packages\tensorflow_core\python\keras\engine\training_utils.py in <listcomp>(.0)
657 y.shape[0]
658 for y in x
--> 659 if y is not None and not is_tensor_or_composite_tensor(y)
660 ])
661
IndexError: tuple index out of range
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'm trying to make own attention model and I found example code in here:
https://www.kaggle.com/takuok/bidirectional-lstm-and-attention-lb-0-043
and it works just fine when I run it without modification.
But my own data contain only numeric values, I had to change example code.
so I erase embedding part in example code and plus, this is what I fixed.
xtr = np.reshape(xtr, (xtr.shape[0], 1, xtr.shape[1]))
# xtr.shape() = (n_sample_train, 1, 150), y.shape() = (n_sample_train, 6)
xte = np.reshape(xte, (xte.shape[0], 1, xte.shape[1]))
# xtr.shape() = (n_sample_test, 1, 150)
model = BidLstm(maxlen, max_features)
model.compile(loss='binary_crossentropy', optimizer='adam',
metrics=['accuracy'])
and my BidLstm func looks like,
def BidLstm(maxlen, max_features):
inp = Input(shape=(1,150))
#x = Embedding(max_features, embed_size, weights=[embedding_matrix], trainable=False)(inp) -> I don't need embedding since my own data is numeric.
x = Bidirectional(LSTM(300, return_sequences=True, dropout=0.25,
recurrent_dropout=0.25))(inp)
x = Attention(maxlen)(x)
x = Dense(256, activation="relu")(x)
x = Dropout(0.25)(x)
x = Dense(6, activation="sigmoid")(x)
model = Model(inputs=inp, outputs=x)
return model
and it said,
InvalidArgumentErrorTraceback (most recent call last)
<ipython-input-62-929955370368> in <module>
29
30 early = EarlyStopping(monitor="val_loss", mode="min", patience=1)
---> 31 model.fit(xtr, y, batch_size=128, epochs=15, validation_split=0.1, callbacks=[early])
32 #model.fit(xtr, y, batch_size=256, epochs=1, validation_split=0.1)
33
/usr/local/lib/python3.5/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, steps_per_epoch, validation_steps, **kwargs)
1037 initial_epoch=initial_epoch,
1038 steps_per_epoch=steps_per_epoch,
-> 1039 validation_steps=validation_steps)
1040
1041 def evaluate(self, x=None, y=None,
/usr/local/lib/python3.5/dist-packages/keras/engine/training_arrays.py in fit_loop(model, f, ins, out_labels, batch_size, epochs, verbose, callbacks, val_f, val_ins, shuffle, callback_metrics, initial_epoch, steps_per_epoch, validation_steps)
197 ins_batch[i] = ins_batch[i].toarray()
198
--> 199 outs = f(ins_batch)
200 outs = to_list(outs)
201 for l, o in zip(out_labels, outs):
/usr/local/lib/python3.5/dist-packages/keras/backend/tensorflow_backend.py in __call__(self, inputs)
2713 return self._legacy_call(inputs)
2714
-> 2715 return self._call(inputs)
2716 else:
2717 if py_any(is_tensor(x) for x in inputs):
/usr/local/lib/python3.5/dist-packages/keras/backend/tensorflow_backend.py in _call(self, inputs)
2673 fetched = self._callable_fn(*array_vals, run_metadata=self.run_metadata)
2674 else:
-> 2675 fetched = self._callable_fn(*array_vals)
2676 return fetched[:len(self.outputs)]
2677
/usr/local/lib/python3.5/dist-packages/tensorflow/python/client/session.py in __call__(self, *args, **kwargs)
1437 ret = tf_session.TF_SessionRunCallable(
1438 self._session._session, self._handle, args, status,
-> 1439 run_metadata_ptr)
1440 if run_metadata:
1441 proto_data = tf_session.TF_GetBuffer(run_metadata_ptr)
/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/errors_impl.py in __exit__(self, type_arg, value_arg, traceback_arg)
526 None, None,
527 compat.as_text(c_api.TF_Message(self.status.status)),
--> 528 c_api.TF_GetCode(self.status.status))
529 # Delete the underlying status object from memory otherwise it stays alive
530 # as there is a reference to status from this from the traceback due to
InvalidArgumentError: Input to reshape is a tensor with 128 values, but the requested shape requires a multiple of 150
[[{{node attention_16/Reshape_2}}]]
[[{{node loss_5/mul}}]]
I think something wrong in loss function saids in here:
Input to reshape is a tensor with 2 * "batch_size" values, but the requested shape has "batch_size"
but I don't know which part to fix it.
my keras and tensorflow versions are 2.2.4 and 1.13.0-rc0
please help. thanks.
Edit 1
I've change my batch size, like keras saids, multiple of 150(batch_size = 150). than it reports
Train on 143613 samples, validate on 15958 samples
Epoch 1/15
143400/143613 [============================>.] - ETA: 0s - loss: 0.1505 - acc: 0.9619
InvalidArgumentError: Input to reshape is a tensor with 63 values, but the requested shape requires a multiple of 150
[[{{node attention_18/Reshape_2}}]]
[[{{node metrics_6/acc/Mean_1}}]]
and details is same as before. what should I do?
Your input shape must be (150,1).
LSTM shapes are (batch, steps, features). It's pointless to use LSTMs with 1 step only. (Unless you are using custom training loops with stateful=True, which is not your case).
I very new to deep learning classification. I have reviews data with the label(pos, neg) and I 'm trying to classify the data using keras. here is my code:
from sklearn.model_selection import train_test_split
from sklearn.feature_extraction.text import CountVectorizer
sentences_train, sentences_test, y_train, y_test =
train_test_split(review_list2, label_list, test_size=0.25, random_state=1000)
vectorizer = CountVectorizer()
vectorizer.fit(sentences_train)
X_train = vectorizer.transform(sentences_train)
X_test = vectorizer.transform(sentences_test)
#build the model
from keras.models import Sequential
from keras import layers
input_dim = X_train.shape[1]
model = Sequential()
model.add(layers.Dense(8, input_dim=input_dim, activation='relu'))
model.add(layers.Dense(2, activation='softmax'))
model.compile(loss='binary_crossentropy',
optimizer='adam', metrics=['accuracy'])
model.summary()
history = model.fit(X_train, y_train, epochs=100, verbose=False, validation_data=(X_test, y_test), batch_size=10)
I got an error:
AttributeError Traceback (most recent call last)
<ipython-input-52-34c39f53e335> in <module>
----> 1 history = model.fit(X_train, y_train, epochs=100, verbose=False, validation_data=(X_test, y_test), batch_size=10)
d:\py-ver35\lib\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
d:\py-ver35\lib\site-packages\keras\engine\training.py in _standardize_user_data(self, x, y, sample_weight, class_weight, check_array_lengths, batch_size)
787 feed_output_shapes,
788 check_batch_axis=False, # Don't enforce the batch size.
--> 789 exception_prefix='target')
790
791 # Generate sample-wise weight values given the `sample_weight` and
d:\py-ver35\lib\site-packages\keras\engine\training_utils.py in standardize_input_data(data, names, shapes, check_batch_axis, exception_prefix)
90 data = data.values if data.__class__.__name__ == 'DataFrame' else data
91 data = [data]
---> 92 data = [standardize_single_array(x) for x in data]
93
94 if len(data) != len(names):
d:\py-ver35\lib\site-packages\keras\engine\training_utils.py in <listcomp>(.0)
90 data = data.values if data.__class__.__name__ == 'DataFrame' else data
91 data = [data]
---> 92 data = [standardize_single_array(x) for x in data]
93
94 if len(data) != len(names):
d:\py-ver35\lib\site-packages\keras\engine\training_utils.py in standardize_single_array(x)
25 'Got tensor with shape: %s' % str(shape))
26 return x
---> 27 elif x.ndim == 1:
28 x = np.expand_dims(x, 1)
29 return x
AttributeError: 'str' object has no attribute 'ndim'
I have tried every solution mention related to this error but still cannot fix.Any help? thanks in Advance