conv_base = EfficientNetB6(weights="imagenet", include_top=False, input_shape=400,400)
SyntaxError: positional argument follows keyword argument
#Options: EfficientNetB0, EfficientNetB1, EfficientNetB2, EfficientNetB3, ... up to 7
#Higher the number, the more complex the model is. and the larger resolutions it can handle, but the more GPU memory it will need# loading pretrained conv base model
#input_shape is (height, width, number of channels) for images
conv_base = EfficientNetB6(weights="imagenet", include_top=False, input_shape=input_shape)
#this is the original code that i found, but i don't know what to put
Firstly, the error you're describing isn't related to EfficientNet or TensorFlow, it's a syntax error. You cannot call positional arguments after keyword arguments. For example:
def foo(x, y):
return x
foo(1, 2) # two positional arguments
foo(x=1, y=2) # two keyword arguments
foo(1, y=2) # a positional argument followed by a keyword
# foo(x=1, 2) # Syntax error. A keyword argument followed by a positional one
Secondly, from reading the documentation you can clearly see:
input_shape: Optional shape tuple, only to be specified if include_top is False. It should have exactly 3 inputs channels.
You're passing on "400", which is not a tuple and doesn't have 3 input channels.
Your input_shape should look something like (height, width, number of channels) - for example (400, 400, 3)
Related
Scikit-Learn RandomForestClassifier throws an error for a multilabel classification problem.
This code creates a RandomForestClassifier multilabel object, given predictors C and multi-labels out with no error.
C = np.array([[2,4,6],[4,2,1],[8,3,1]])
out = np.array([[0,1],[0,1],[1,0]])
rf = RandomForestClassifier(n_estimators=100, oob_score=True)
rf.fit(C,out)
If I modify the multilabels, so that all the elements at a certain index are the same, say (where all the first components of the multilabels equals zero)
out = np.array([[0,1],[0,1],[0,0]])
I get an error and traceback:
VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a
list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated.
If you meant to do this, you must specify 'dtype=object' when creating the ndarray.
y_pred = np.array(y_pred, copy=False)
raise ValueError(
507 "The type of target cannot be used to compute OOB "
508 f"estimates. Got {y_type} while only the following are "
509 "supported: continuous, continuous-multioutput, binary, "
510 "multiclass, multilabel-indicator."
511 )
ValueError: could not broadcast input array from shape (2,1) into shape (2,)
Not requesting OOB predictions does not result in an error:
rf_err = RandomForestClassifier(n_estimators=100, oob_score=False)
I cannot figure out why keeping the OOB predictions would trigger such an error, when all the n-component of a multilabel are equal.
In your setup out_err = np.array([[0,1],[0,1],[0,0]]) you do not have any examples of the second class, so you only have elements of 1 class.
That means that there is no 'class label' dimension and it can be omitted. That's why you see (2,) shape.
Please, describe your initial intent: why would you need to set a particular position in labels to 0. If you try to go with N-1 classes instead of N classes I suggest removing the position itself and the elements of the class from the dataset, not putting all zeros:
out=[[1,0,0],[0,1,0],[0,1,0],[0,0,1],[1,0,0]] # 3 classes
# remove the second class:
out=[[1,0],[0,1],[1,0]] # 2 classes
I am getting the error "dtw() got an unexpected keyword argument 'dist'" while I'm trying to calculate the dtw of 2 wav files. I can't figure out why or what to do to fix it. I am attaching the code below.
import librosa
import librosa.display
y1, sr1 = librosa.load('sample_data/Abir_Arshad_22.wav')
y2, sr2 = librosa.load('sample_data/Abir_Arshad_22.wav')
%pylab inline
subplot(1, 2, 1)
mfcc1 = librosa.feature.mfcc(y1, sr1)
librosa.display.specshow(mfcc1)
subplot(1, 2, 2)
mfcc2 = librosa.feature.mfcc(y2, sr2)
librosa.display.specshow(mfcc2)
from dtw import dtw
from numpy.linalg import norm
dist, cost, acc_cost, path = dtw(mfcc1.T, mfcc2.T, dist=lambda x, y: norm(x - y, ord=1))
print ('Normalized distance between the two sounds:', dist)
the error is occurring in the 2nd last line.
The error message is straight forward. Lets read the docs of the method you are calling:
https://dynamictimewarping.github.io/py-api/html/api/dtw.dtw.html#dtw.dtw
The dtw function has the following parameters:
Parameters x – query vector or local cost matrix
y – reference vector, unused if x given as cost matrix
dist_method – pointwise (local) distance function to use.
step_pattern – a stepPattern object describing the local warping steps
allowed with their cost (see [stepPattern()])
window_type – windowing function. Character: “none”, “itakura”,
“sakoechiba”, “slantedband”, or a function (see details).
open_begin,open_end – perform open-ended alignments
keep_internals – preserve the cumulative cost matrix, inputs, and
other internal structures
distance_only – only compute distance (no backtrack, faster)
You try to pass an argument named dist and that argument simply is not known.
Instead, removing that argument would solve the issue, such as
dist, cost, acc_cost, path = dtw(mfcc1.T, mfcc2.T)
I want to use the Crossentropyloss of pytorch but somehow my code only works with batchsize 2, so i am asuming there is something wrong with the shapes of target and output.
I get following error:
Value Error: Expected target size (50, 2), got torch.Size([50, 3])
My targetsize is (N=50,batchsize=3) and the output of my model is (N=50, batchsize=3, number of classes =2). Before the output layer my shape is (N=50,batchsize=3,dimensions=64).
How do i need to change the shapes so that the Crossentropyloss works?
Without further information about your model, here's what I would do. You have a many-to-many RNN which outputs (seq_len, batch_size, nb_classes) and the target is (seq_len, seq_len). The nn.CrossEntropyLoss module can take additional dimensions (batch_size, nb_classes, d1, d2, ..., dK) as an input.
You could make it work by permuting the axes, such that the outputted tensor is of shape (batch_size, nb_classes, seq_len). This should make it happen:
output = output.permute(0, 2, 1)
Additionally, your target will also have to change to be (batch_size, seq_len):
target = target.permute(1, 0)
I want to have input layer as 8bit integer, to avoid int->float conversion on CPU:
ITensor* data = network->addInput(
m_InputBlobName.c_str(), nvinfer1::DataType::kINT8,
DimsCHW{static_cast<int>(m_InputC), static_cast<int>(m_InputH),
static_cast<int>(m_InputW)});
but it gives me this error message:
[E] [TRT] Parameter check failed at: ../builder/Network.cpp::addInput::466, condition: type != DataType::kINT8
Is it possible to make it work, or INT8 is only intended to be used for approximate calculations?
I found python api with addInput description:
add_input()
addInput(const char *name, DataType type, Dims dimensions)=0 -> ITensor *
Add an input tensor to the network.
The name of the input tensor is used to find the index into the buffer array for an engine built from the network.
Parameters:
name (*) – The name of the tensor.
type (*) – The type of the data held in the tensor.
dimensions (*) – The dimensions of the tensor.
Only DataType::kFLOAT, DataType::kHALF and DataType::kINT32 are valid input tensor types. The volume of the dimensions, including the maximum batch size, must be less than 2^30 elements.
Returns: The new tensor or None if there is an error.
TensorFlow provides the possibility for combining ValidationMonitors with several predefined estimators like tf.contrib.learn.DNNClassifier.
But I want to use a ValidationMonitor for my own estimator which I have created based on 1.
For my own estimator I initialize first a ValidationMonitor:
validation_monitor = tf.contrib.learn.monitors.ValidationMonitor(testX,testY,every_n_steps=50)
estimator = tf.contrib.learn.Estimator(model_fn=model,model_dir=direc,config=tf.contrib.learn.RunConfig(save_checkpoints_secs=1))
input_fn = tf.contrib.learn.io.numpy_input_fn({"x": x}, y, 4, num_epochs=1000)
Here I pass the monitor as shown in 2 for tf.contrib.learn.DNNClassifier:
estimator.fit(input_fn=input_fn, steps=1000,monitors=[validation_monitor])
This fails and following error was printed:
ValueError: Features are incompatible with given information. Given features: Tensor("input:0", shape=(?, 1), dtype=float64), required signatures: {'x': TensorSignature(dtype=tf.float64, shape=TensorShape([Dimension(None)]), is_sparse=False)}.
How can I use monitors for my own estimators?
Thanks.
Problem is solved when passing input_fn containing testX and testY to ValidationMonitor instead of passing the tensors testX and testY directly.
For the record, your error was caused by the fact that ValidationMonitor expects x to be a dictionary like { 'feature_name_as_a_string' : feature_tensor }, which in your input_fn is done internally by the call to tf.contrib.learn.io.numpy_input_fn(...).
More information about how to build features dictionaries can be found in the Building Input Functions with tf.contrib.learn article of the documentation.