I'm getting the following error when trying to extract festues. The y contains the labels so not sure why I'm getting the error
Code:
for cls in os.listdir(path):
for sound in tqdm(os.listdir(os.path.join(path, cls))):
wav = librosa.load(os.path.join(os.path.join(path, cls, sound)), sr=16000)[0].astype(np.float32)
tmp_samples.append(wav)
tmp_labels.append(cls)
X_train, y_train , X_test , y_test = train_test_split( tmp_samples, tmp_labels , test_size=0.60,shuffle=True)
for x,y in zip(X_test,y_test):
extract_features(x, y, model, plain_samples , plain_labels )
def extract_features(wav, cls, model, plain_samples, plain_labels):
for feature in model(wav)[1]:
plain_samples.append(feature)
plain_labels.append(cls)
Error:
Traceback (most recent call last):
File "optunaCopy.py", line 481, in <module>
main(sys.argv[1:])
File "optunaCopy.py", line 397, in main
X_train, y_train , X_test , y_test,X_valid,y_valid = create_dataset(path)
File "optunaCopy.py", line 93, in create_dataset
extract_features(x, y, model, plain_samples , plain_labels )
File "optunaCopy.py", line 321, in extract_features
for feature in model(wav)[1]:
File "C:\Users\XXX\anaconda3\envs\yamnet\lib\site-packages\keras\engine\base_layer.py", line 1020, in __call__
input_spec.assert_input_compatibility(self.input_spec, inputs, self.name)
File "C:\Users\XXX\anaconda3\envs\yamnet\lib\site-packages\keras\engine\input_spec.py", line 196, in assert_input_compatibility
raise TypeError('Inputs to a layer should be tensors. Got: %s' % (x,))
TypeError: Inputs to a layer should be tensors. Got:hi
Related
I get the following error when trying to test the yolov5.onxx model with opencv
File "yolov5_opnecv.py", line 119, in <module>
outs = detect(inputImage, net)
File "yolov5_opnecv.py", line 28, in detect
preds = net.forward()
cv2.error: OpenCV(4.5.4) /tmp/pip-req-build-24v3eisa/opencv/modules/dnn/src/layers/reshape_layer.cpp:106: error: (-215:Assertion failed) total(srcShape, srcRange.start, srcRange.end) == maskTotal in function 'computeShapeByReshapeMask'
blob = cv2.dnn.blobFromImage(image, 1/255.0, (INPUT_WIDTH, INPUT_HEIGHT), swapRB=True, crop=False)
On this line the input image dimensions should be the same ones as the network was trained on. Mine was 640x640
I have created a feed forward neural network but but it is giving a Type Error despite changing the datatype of the parameter. I am really new to keras and Machine Learning so I would appreciate as detailed help as possible. I am attaching the code snippet and the error log below. CODE-
num_of_features = X_train.shape[1]
nb_classes = Y_train.shape[1]
def baseline_model():
def branch2(x):
x = Dense(np.floor(num_of_features*50), activation='sigmoid')(x)
x = Dropout(0.75)(x)
x = Dense(np.floor(num_of_features*20), activation='sigmoid')(x)
x = Dropout(0.5)(x)
x = Dense(np.floor(num_of_features), activation='sigmoid')(x)
x = Dropout(0.1)(x)
return x
main_input = Input(shape=(num_of_features,), name='main_input')
x = main_input
x = branch2(x)
main_output = Dense(nb_classes, activation='softmax')(x)
model = Model(input=main_input, output=main_output)
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy', 'categorical_crossentropy'])
return model
model = baseline_model()
ERROR-
Traceback (most recent call last):
File "h2_fit_neural.py", line 143, in <module>
model = baseline_model()
File "h2_fit_neural.py", line 137, in baseline_model
x = branch2(x)
File "h2_fit_neural.py", line 124, in branch2
x = Dense(np.floor(num_of_features*50), activation='sigmoid')(x)
File "/home/shashank/tensorflow/lib/python3.6/site-packages/keras/engine/base_layer.py", line 432, in __call__
self.build(input_shapes[0])
File "/home/shashank/tensorflow/lib/python3.6/site-packages/keras/layers/core.py", line 872, in build
constraint=self.kernel_constraint)
File "/home/shashank/tensorflow/lib/python3.6/site-packages/keras/legacy/interfaces.py", line 91, in wrapper
return func(*args, **kwargs)
File "/home/shashank/tensorflow/lib/python3.6/site-packages/keras/engine/base_layer.py", line 249, in add_weight
weight = K.variable(initializer(shape),
File "/home/shashank/tensorflow/lib/python3.6/site-packages/keras/initializers.py", line 218, in __call__
dtype=dtype, seed=self.seed)
File "/home/shashank/tensorflow/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py", line 4077, in random_uniform
dtype=dtype, seed=seed)
File "/home/shashank/tensorflow/lib/python3.6/site-packages/tensorflow/python/ops/random_ops.py", line 242, in random_uniform
rnd = gen_random_ops.random_uniform(shape, dtype, seed=seed1, seed2=seed2)
File "/home/shashank/tensorflow/lib/python3.6/site-packages/tensorflow/python/ops/gen_random_ops.py", line 674, in random_uniform
name=name)
File "/home/shashank/tensorflow/lib/python3.6/site-packages/tensorflow/python/framework/op_def_library.py", line 609, in _apply_op_helper
param_name=input_name)
File "/home/shashank/tensorflow/lib/python3.6/site-packages/tensorflow/python/framework/op_def_library.py", line 60, in _SatisfiesTypeConstraint
", ".join(dtypes.as_dtype(x).name for x in allowed_list)))
TypeError: Value passed to parameter 'shape' has DataType float32 not in list of allowed values: int32, int64
Why are you using np.floor for the shape in your Dense layers? This will produce a float, you need an int there. Removing np.floor should solve your problem.
I am trying to write a machine learning program. The idea was to train a model (defined in q_model) which could be trained with RMSProp. I report here a really simplified version of my code, which is not working.
import tensorflow as tf
import numpy as np
#--------------------------------------
# Model definition
#--------------------------------------
# Let's use a simple nn for the Q value function
W = tf.Variable(tf.random_normal([3,10],dtype=tf.float64), name='W')
b = tf.Variable(tf.random_normal([10],dtype=tf.float64), name='b')
def q_model(X,A):
input = tf.concat((X,A), axis=1)
return tf.reduce_sum( tf.nn.relu(tf.matmul(input, W) + b), axis=1)
#--------------------------------------
# Model and model initializer
#--------------------------------------
optimizer = tf.train.RMSPropOptimizer(0.9)
init = tf.initialize_all_variables()
sess = tf.Session()
sess.run(init)
#--------------------------------------
# Learning
#--------------------------------------
x = np.matrix(np.random.uniform((0.,0.),(1.,1.), (1000,2)))
a = np.matrix(np.random.uniform((0),(1), 1000)).T
y = np.matrix(np.random.uniform((0),(1), 1000)).T
y_batch , x_batch, a_batch = tf.placeholder("float64",shape=(None,1), name='y'), tf.placeholder("float64",shape=(None,2), name='x'), tf.placeholder("float64",shape=(None,1), name='a')
error = tf.reduce_sum(tf.square(y_batch - q_model(x_batch,a_batch))) / 100.
train = optimizer.minimize(error)
indx = range(1000)
for i in range(100):
# batches
np.random.shuffle(indx)
indx = indx[:100]
print sess.run({'train':train}, feed_dict={'x:0':x[indx],'a:0':a[indx],'y:0':y[indx]})
The error is:
Traceback (most recent call last):
File "/home/samuele/Projects/GBFQI/test/tf_test.py", line 45, in <module>
print sess.run({'train':train}, feed_dict={'x:0':x[indx],'a:0':a[indx],'y:0':y[indx]})
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.py", line 789, in run
run_metadata_ptr)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.py", line 997, in _run
feed_dict_string, options, run_metadata)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.py", line 1132, in _do_run
target_list, options, run_metadata)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.py", line 1152, in _do_call
raise type(e)(node_def, op, message)
tensorflow.python.framework.errors_impl.FailedPreconditionError: Attempting to use uninitialized value b/RMSProp
[[Node: RMSProp/update_b/ApplyRMSProp = ApplyRMSProp[T=DT_DOUBLE, _class=["loc:#b"], use_locking=false, _device="/job:localhost/replica:0/task:0/cpu:0"](b, b/RMSProp, b/RMSProp_1, RMSProp/update_b/Cast, RMSProp/update_b/Cast_1, RMSProp/update_b/Cast_2, RMSProp/update_b/Cast_3, gradients/add_grad/tuple/control_dependency_1)]]
Caused by op u'RMSProp/update_b/ApplyRMSProp', defined at:
File "/home/samuele/Projects/GBFQI/test/tf_test.py", line 38, in <module>
train = optimizer.minimize(error)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/training/optimizer.py", line 325, in minimize
name=name)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/training/optimizer.py", line 456, in apply_gradients
update_ops.append(processor.update_op(self, grad))
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/training/optimizer.py", line 97, in update_op
return optimizer._apply_dense(g, self._v) # pylint: disable=protected-access
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/training/rmsprop.py", line 140, in _apply_dense
use_locking=self._use_locking).op
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/training/gen_training_ops.py", line 449, in apply_rms_prop
use_locking=use_locking, name=name)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/op_def_library.py", line 767, in apply_op
op_def=op_def)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/ops.py", line 2506, in create_op
original_op=self._default_original_op, op_def=op_def)
File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/ops.py", line 1269, in __init__
self._traceback = _extract_stack()
FailedPreconditionError (see above for traceback): Attempting to use uninitialized value b/RMSProp
[[Node: RMSProp/update_b/ApplyRMSProp = ApplyRMSProp[T=DT_DOUBLE, _class=["loc:#b"], use_locking=false, _device="/job:localhost/replica:0/task:0/cpu:0"](b, b/RMSProp, b/RMSProp_1, RMSProp/update_b/Cast, RMSProp/update_b/Cast_1, RMSProp/update_b/Cast_2, RMSProp/update_b/Cast_3, gradients/add_grad/tuple/control_dependency_1)]]
I cannot explain myself this error since the model is initialized, and actually if I run
print sess.run(q_model(x,a))
the model is working as expected without raising any error.
EDIT:
My question is different from this question. I was already aware of
init = tf.initialize_all_variables()
sess = tf.Session()
sess.run(init)
but I didn't know that it should have been performed after the optimization too.
You need to put this piece of code:
init = tf.initialize_all_variables()
sess = tf.Session()
sess.run(init)
after having created these tensors:
y_batch , x_batch, a_batch = tf.placeholder("float64",shape=(None,1), name='y'), tf.placeholder("float64",shape=(None,2), name='x'), tf.placeholder("float64",shape=(None,1), name='a')
error = tf.reduce_sum(tf.square(y_batch - q_model(x_batch,a_batch))) / 100.
train = optimizer.minimize(error)
init = tf.initialize_all_variables()
sess = tf.Session()
sess.run(init)
Otherwise the hidden variables added to the Graph when calling the optimiser.minimize method won't be initialised.
Meantime, the call to print sess.run(q_model(x,a)) works because the variables used by this part of the Graph have been all initialised.
BTW: Use tf.global_variables_initializer rather than tf.initialize_all_variables.
EDIT:
To perform a selective initialisation, you could do something like that:
with tf.variable_scope("to_be_initialised"):
train = optimizer.minimize(error)
sess.run(tf.variables_initializer(tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope='to_be_initialised')))
This is my first post. I've been trying to combine features with FeatureUnion and Pipeline, but when I add a tf-idf + svd piepline the test fails with a 'dimension mismatch' error. My simple task is to create a regression model to predict search relevance. Code and errors are reported below. Is there something wrong in my code?
df = read_tsv_data(input_file)
df = tokenize(df)
df_train, df_test = train_test_split(df, test_size = 0.2, random_state=2016)
x_train = df_train['sq'].values
y_train = df_train['relevance'].values
x_test = df_test['sq'].values
y_test = df_test['relevance'].values
# char ngrams
char_ngrams = CountVectorizer(ngram_range=(2,5), analyzer='char_wb', encoding='utf-8')
# TFIDF word ngrams
tfidf_word_ngrams = TfidfVectorizer(ngram_range=(1, 4), analyzer='word', encoding='utf-8')
# SVD
svd = TruncatedSVD(n_components=100, random_state = 2016)
# SVR
svr_lin = SVR(kernel='linear', C=0.01)
pipeline = Pipeline([
('feature_union',
FeatureUnion(
transformer_list = [
('char_ngrams', char_ngrams),
('char_ngrams_svd_pipeline', make_pipeline(char_ngrams, svd)),
('tfidf_word_ngrams', tfidf_word_ngrams),
('tfidf_word_ngrams_svd', make_pipeline(tfidf_word_ngrams, svd))
]
)
),
('svr_lin', svr_lin)
])
model = pipeline.fit(x_train, y_train)
y_pred = model.predict(x_test)
When adding the pipeline below to the FeatureUnion list:
('tfidf_word_ngrams_svd', make_pipeline(tfidf_word_ngrams, svd))
The exception below is generated:
2016-07-31 10:34:08,712 : Testing ... Test Shape: (400,) - Training Shape: (1600,)
Traceback (most recent call last):
File "src/model/end_to_end_pipeline.py", line 236, in <module>
main()
File "src/model/end_to_end_pipeline.py", line 233, in main
process_data(input_file, output_file)
File "src/model/end_to_end_pipeline.py", line 175, in process_data
y_pred = model.predict(x_test)
File "/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/sklearn/utils/metaestimators.py", line 37, in <lambda>
out = lambda *args, **kwargs: self.fn(obj, *args, **kwargs)
File "/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/sklearn/pipeline.py", line 203, in predict
Xt = transform.transform(Xt)
File "/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/sklearn/pipeline.py", line 523, in transform
for name, trans in self.transformer_list)
File "/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/sklearn/externals/joblib/parallel.py", line 800, in __call__
while self.dispatch_one_batch(iterator):
File "/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/sklearn/externals/joblib/parallel.py", line 658, in dispatch_one_batch
self._dispatch(tasks)
File "/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/sklearn/externals/joblib/parallel.py", line 566, in _dispatch
job = ImmediateComputeBatch(batch)
File "/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/sklearn/externals/joblib/parallel.py", line 180, in __init__
self.results = batch()
File "/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/sklearn/externals/joblib/parallel.py", line 72, in __call__
return [func(*args, **kwargs) for func, args, kwargs in self.items]
File "/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/sklearn/pipeline.py", line 399, in _transform_one
return transformer.transform(X)
File "/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/sklearn/utils/metaestimators.py", line 37, in <lambda>
out = lambda *args, **kwargs: self.fn(obj, *args, **kwargs)
File "/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/sklearn/pipeline.py", line 291, in transform
Xt = transform.transform(Xt)
File "/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/sklearn/decomposition/truncated_svd.py", line 201, in transform
return safe_sparse_dot(X, self.components_.T)
File "/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/sklearn/utils/extmath.py", line 179, in safe_sparse_dot
ret = a * b
File "/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/scipy/sparse/base.py", line 389, in __mul__
raise ValueError('dimension mismatch')
ValueError: dimension mismatch
What if you change second svd usage to new svd?
transformer_list = [
('char_ngrams', char_ngrams),
('char_ngrams_svd_pipeline', make_pipeline(char_ngrams, svd)),
('tfidf_word_ngrams', tfidf_word_ngrams),
('tfidf_word_ngrams_svd', make_pipeline(tfidf_word_ngrams, clone(svd)))
]
Seems your problem occurs because you're using same object 2 times. I is fitted first time on CountVectorizer, and second time on TfidfVectorizer (Or vice versa), and after you call predict of whole pipeline this svd object cannot understand output of CountVectorizer, because it was fitted on or TfidfVectorizer's output (Or again, vice versa).
I am working on Gaussian Process Regression with Python on NIR spectrum data. I can get some results with GPR and would like to optimize parameters for GPR. I am trying to use GridSearchCV to optimize parameters, but I keep getting an error and could not find any examples that people used GridSearchCV for Gaussian Process (from sklearn.gaussian_process). My quick question is if I can use GridSearchCV for GPR. If not, what would you recommend to use to optimize parameters.
This is my error:
---------------------------------------------------
-# Tuning hyper-parameters for precision
Traceback (most recent call last):
File "", line 1, in runfile('C:/Users/hkim.N04485/Desktop/Python/untitled14.py', wdir='C:/Users/hkim.N04485/Desktop/Python')
File "C:\Users\hkim.N04485\Anaconda2\lib\site-packages\spyderlib\widgets\externalshell\sitecustomize.py", line 699, in runfile execfile(filename, namespace)
File "C:\Users\hkim.N04485\Anaconda2\lib\site-packages\spyderlib\widgets\externalshell\sitecustomize.py", line 74, in execfile exec(compile(scripttext, filename, 'exec'), glob, loc)
File "C:/Users/hkim.N04485/Desktop/Python/untitled14.py", line 39, in gp.fit(X1, y1_glucose)
File "C:\Users\hkim.N04485\Anaconda2\lib\site-packages\sklearn\grid_search.py", line 804, in fit return self._fit(X, y, ParameterGrid(self.param_grid))
File "C:\Users\hkim.N04485\Anaconda2\lib\site-packages\sklearn\grid_search.py", line 553, in _fit for parameters in parameter_iterable
File "C:\Users\hkim.N04485\Anaconda2\lib\site-packages\sklearn\externals\joblib\parallel.py", line 804, in call while self.dispatch_one_batch(iterator):
File "C:\Users\hkim.N04485\Anaconda2\lib\site-packages\sklearn\externals\joblib\parallel.py", line 662, in dispatch_one_batch self._dispatch(tasks)
File "C:\Users\hkim.N04485\Anaconda2\lib\site-packages\sklearn\externals\joblib\parallel.py", line 570, in _dispatch job = ImmediateComputeBatch(batch)
File "C:\Users\hkim.N04485\Anaconda2\lib\site-packages\sklearn\externals\joblib\parallel.py", line 183, in init self.results = batch()
File "C:\Users\hkim.N04485\Anaconda2\lib\site-packages\sklearn\externals\joblib\parallel.py", line 72, in call return [func(*args, **kwargs) for func, args, kwargs in self.items]
File "C:\Users\hkim.N04485\Anaconda2\lib\site-packages\sklearn\cross_validation.py", line 1550, in _fit_and_score test_score = _score(estimator, X_test, y_test, scorer)
File "C:\Users\hkim.N04485\Anaconda2\lib\site-packages\sklearn\cross_validation.py", line 1606, in _score score = scorer(estimator, X_test, y_test)
File "C:\Users\hkim.N04485\Anaconda2\lib\site-packages\sklearn\metrics\scorer.py", line 90, in call **self._kwargs)
File "C:\Users\hkim.N04485\Anaconda2\lib\site-packages\sklearn\metrics\classification.py", line 1203, in precision_score sample_weight=sample_weight)
File "C:\Users\hkim.N04485\Anaconda2\lib\site-packages\sklearn\metrics\classification.py", line 956, in precision_recall_fscore_support y_type, y_true, y_pred = _check_targets(y_true, y_pred)
File "C:\Users\hkim.N04485\Anaconda2\lib\site-packages\sklearn\metrics\classification.py", line 82, in _check_targets "".format(type_true, type_pred))
ValueError: Can't handle mix of multiclass and continuous
How do I fix this?
Here is my code.
tuned_parameters = [{'corr':['squared_exponential'], 'theta0': [0.01, 0.2, 0.8, 1.]},
{'corr':['cubic'], 'theta0': [0.01, 0.2, 0.8, 1.]}]
scores = ['precision', 'recall']
xy_line=(0,1200)
for score in scores:
print("# Tuning hyper-parameters for %s" % score)
print()
gp = GridSearchCV(GaussianProcess(normalize=False), tuned_parameters, cv=5,
scoring='%s_weighted' % score)
gp.fit(X1, y1_glucose)
print("Best parameters set found on development set:")
print()
print(gp.best_params_)
print()
print("Grid scores on development set:")
print()
for params, mean_score, scores in gp.grid_scores_:
print("%0.3f (+/-%0.03f) for %r"
% (mean_score, scores.std() * 2, params))
y_true, y_pred = y2_glucose, gp.predict(X2)
# Scatter plot (reference vs predicted )
fig, ax = plt.subplots(figsize=(11,13))
ax.scatter(y2_glucose,y_pred)
ax.plot(xy_line, xy_line, 'r--')
major_ticks = np.arange(-300,2000,100)
minor_ticks = np.arange(0,1201,100)
ax.set_xticks(minor_ticks)
ax.set_yticks(major_ticks)
ax.grid()
plt.title('1')
ax.set_xlabel('Reference')
ax.set_ylabel('Predicted')