contrastive | Contrastive PCA -

by abidlabs Jupyter Notebook Version: 1.2.0 License: MIT

X-Ray Key Features Code Snippets(10)Community Discussions Vulnerabilities Install Support

kandi X-RAY | contrastive Summary

contrastive is a Jupyter Notebook library. contrastive has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. You can download it from GitHub.

Contrastive PCA

Support

Quality

Security

License

Reuse

Support

contrastive has a low active ecosystem.

It has 149 star(s) with 41 fork(s). There are 8 watchers for this library.

It had no major release in the last 12 months.

There are 9 open issues and 9 have been closed. On average issues are closed in 24 days. There are 1 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of contrastive is 1.2.0

Quality

contrastive has no bugs reported.

Security

contrastive has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

License

contrastive is licensed under the MIT License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

Reuse

contrastive releases are not available. You will need to build from source code and install.

Top functions reviewed by kandi - BETA

kandi's functional review helps you automatically verify the functionalities of the libraries and avoid rework.
Currently covering the most popular Java, JavaScript and Python libraries. See a Sample of contrastive

Get all kandi verified functions for this library.

contrastive Key Features

No Key Features are available at this moment for contrastive.

contrastive Examples and Code Snippets

Compute logits .

python

Lines of Code : 183

License : Non-SPDX (Apache License 2.0)

Copy

def _compute_sampled_logits(weights,
                            biases,
                            labels,
                            inputs,
                            num_sampled,
                            num_classes,

Compute the nce loss .

python

Lines of Code : 108

License : Non-SPDX (Apache License 2.0)

Copy

def nce_loss(weights,
             biases,
             labels,
             inputs,
             num_sampled,
             num_classes,
             num_true=1,
             sampled_values=None,
             remove_accidental_hits=False,

NCE loss .

python

Lines of Code : 101

License : Non-SPDX (Apache License 2.0)

Copy

def nce_loss_v2(weights,
                biases,
                labels,
                inputs,
                num_sampled,
                num_classes,
                num_true=1,
                sampled_values=None,
                remove_acciden

siamese network not getting trained

Python

Lines of Code : 5

License : Strong Copyleft (CC BY-SA 4.0)

Copy

distance = Lambda(euclidean_distance, output_shape=eucl_dist_output_shape)([processed_a, processed_b])

output = Dense(1,activation='sigmoid')(distance)
model = Model(inputs=[input_a, input_b], outputs=output)

Understanding the Keras MNIST Siamese Network and adapting it for triples

Python

Lines of Code : 6

License : Strong Copyleft (CC BY-SA 4.0)

Copy

merged_vector = concatenate([processed_a, processed_b, processed_c], axis=-1, name='merged_layer')

d1 = y_pred[:,0:128]
d2 = y_pred[:,128:256]
d3 = y_pred[:,256:384]

How to compile a keras model that has 2 outputs with a custom loss that takes 3 parameters?

Python

Lines of Code : 17

License : Strong Copyleft (CC BY-SA 4.0)

Copy

final_output = Lambda(lambda x: tf.stack(x, axis=0))([output1, output2])

def contrastive_loss(y_true, y_pred):
    y_pred1 = y_pred[0]
    y_pred2 = y_pred[1]

    '''Contrastive loss from Hadsell-et-al.'06
    htt

How to access sample weights in a Keras custom loss function supplied by a generator?

Python

Lines of Code : 30

License : Strong Copyleft (CC BY-SA 4.0)

Copy

weights = K.variable(np.zeros((batch_size,)))

def custom_loss(true, pred):
    return someCalculation(true, pred, weights)

for e in range(epochs):
    for s in range(steps_per_epoch):

Siamese model not learning anything, always encodes the image into a vector of zeros

Python

Lines of Code : 10

License : Strong Copyleft (CC BY-SA 4.0)

Copy

# distance between the anchor and the positive
pos_dist = K.sum(K.square(anchor-positive),axis=1)

# distance between the anchor and the negative
neg_dist = K.sum(K.square(anchor-negative),axis=1)

# compute loss
basic_loss = pos_dist-neg_

While performing contrastive divergnece for the Boltzmann machine, i am getting the following error. i am using the latest version of pytorch

Python

Lines of Code : 2

License : Strong Copyleft (CC BY-SA 4.0)

Copy

self.W += (torch.mm(v0.t(), ph0) - torch.mm(vk.t(), phk)).t()

roc_auc_score is coming as 0 test accuracy 97% possible?

Python

Lines of Code : 3

License : Strong Copyleft (CC BY-SA 4.0)

Copy

>>> metrics.roc_auc_score(tr_y, 1.0 - y_pred)
0.9954217433041488

Community Discussions

No Community Discussions are available at this moment for contrastive.Refer to stack overflow page for discussions.

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install contrastive

You can download it from GitHub.

Support

For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

Find more information at: