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Results tagged with tensorflow
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TensorFlow is an open source library for machine learning and machine intelligence. TensorFlow uses data flow graphs with tensors flowing along edges. For details, see https://www.tensorflow.org. TensorFlow is released under an Apache 2.0 License.
4
votes
Accepted
Tensorflow 2 eager vs graph mode
()
my_function = eager_function
else:
my_function = graph_function
# You proceed to my_function from now on
I don't know if there is a better way but I've seen this a lot being used in the tensorflow …
- 8,038
6
votes
Accepted
What is the advantage of a tensorflow.data.Dataset over a tensorflow.Tensor?
The main advantage is in domains where you can't fit all of your data into memory.
However, I've seen improvements in performance even in cases where I have all my data into memory. I think two reason …
- 8,038
2
votes
Accepted
Keras model.predict giving different shape from training label array
The 10 outputs came from the fact that you have 10 neurons in the final layer of your network.
If you change your model to
model = tf.keras.models.Sequential([
tf.keras.layers.Dense(10, activation …
- 8,038
1
vote
Accepted
Layer weights don't match in keras
I took the liberty of changing your code a bit to make this a bit more clear.
import numpy as np
import tensorflow as tf
f = lambda x: 2*x
Xtrain = np.random.rand(400, 5) # 5 input features
ytrain = …
- 8,038
1
vote
Tensorflow with Python code
This uses a tensorflow module called AutoGraph to basically convert your python/numpy operations to tensorflow ops.
However not all python operations can be converted. …
- 8,038
0
votes
Accepted
is there a way to customize my loss function to increase recall in one class only?
TensorFlow
In TensorFlow you can do this simply by using this softmax_cross_entropy loss instead of the one you are currently using. This supports class weights (weights attribute). …
- 8,038
1
vote
GradientTape not computing gradient
Since you are trying to compute $\partial loss \over \partial x$ you need to perform all operations that lead from $x$ to $loss$ inside GradientTape's scope, so that it can monitor them.
x = tf.Varia …
- 8,038
1
vote
Accepted
Scheduler for activation layer parameter using Keras callback
You will need to write a custom callback for this, that implements the on_epoch_end method. Roughly it should look something like this
class CustomCallback(keras.callbacks.Callback):
def __init__ …
- 8,038
2
votes
Predict_proba for Binary classifier in Tensorflow
If you're referring to scikit-learn's predict_proba, it is equivalent to taking the sigmoid-activated output of the model in tensorflow. In fact that's exactly what scikit-learn does. …
- 8,038
13
votes
Should use sklearn or tensorflow for neural networks?
Among the two, since you are interested in deep learning, pick tensorflow.
However, I would suggest going with keras, which uses tensorflow as a backend, but offers an easier interface. …
- 8,038
2
votes
Is it possible to make use of the CPU RAM, if i'm running of of VRAM, in tensorflow?
Some options:
Some older tensorflow APIs supported this functionality (e.g. dynamic_rnn - see swap_memory parameter). …
- 8,038
1
vote
Accepted
When does Adam update its weights?
Adam works in the same way as SGD does in this regard, it updates the weights at the end of each iteration, so at the end of an epoch multiple weight updates have been applied.
Inherently neither Adam …
- 8,038
5
votes
Train a GAN on "before and after" images of dental surgeries
It's a very specific problem and there's no right or wrong solution. I'll just write what I'd do in your position and hope that it is useful.
How many "before and after" images will I need?
You …
- 8,038
8
votes
L2 regularization increase the loss rate of the deep learning model
Suppose a neural network with a regular loss function.
$$
\sum_{i=1}^N L \left( y_i, \; \hat y_i \right)
$$
Here, $y_i$ is label for the $i$-th example, while $\hat y_i$ is the model's prediction fo …
- 8,038
8
votes
Accepted
How to make two parallel convolutional neural networks in Keras?
You essentially need a multi-input model. This can only be done through keras' functional api and can work with the pretrained nets in keras.applications. To create one you can do this:
from keras.la …
- 8,038