# About Keras layers All Keras layers have a number of methods in common: – `layer.get_weights()`: returns the weights of the layer as a list of Numpy arrays. – `layer.set_weights(weights)`: sets the weights of the layer from a list of Numpy arrays (with the same shapes as the output of `get_weights`). – `layer.get_config()`: returns a […]
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# Keras FAQ: Frequently Asked Keras Questions – [How should I cite Keras?](#how-should-i-cite-keras) – [How can I run Keras on GPU?](#how-can-i-run-keras-on-gpu) – [How can I run a Keras model on multiple GPUs?](#how-can-i-run-a-keras-model-on-multiple-gpus) – [What does “sample”, “batch”, “epoch” mean?](#what-does-sample-batch-epoch-mean) – [How can I save a Keras model?](#how-can-i-save-a-keras-model) – [Why is the training loss much higher
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svm cnn roc November 29, 2018 In [1]: import pandas as pd import numpy as np from sklearn.decomposition import PCA from sklearn.svm import SVC from collections import Counter import matplotlib.pyplot as plt from matplotlib.pyplot import savefig from tqdm import tqdm_notebook from sklearn.model_selection import GridSearchCV, StratifiedKFold, KFold from itertools import cycle from sklearn import svm, datasets
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## pad_sequences “`python keras.preprocessing.sequence.pad_sequences(sequences, maxlen=None, dtype=’int32′, padding=’pre’, truncating=’pre’, value=0.) “` Transform a list of `num_samples` sequences (lists of scalars) into a 2D Numpy array of shape `(num_samples, num_timesteps)`. `num_timesteps` is either the `maxlen` argument if provided, or the length of the longest sequence otherwise. Sequences that are shorter than `num_timesteps` are padded with `value` at
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dl (Gaussian process classifiers and CNN uncertainty). Convolutional neural networks (CNNs) achieve state-of-the-art performance on image classification tasks, but provide no measure of confidence in their predictions. Use the Keras MNIST example CNN available at https://github.com/ fchollet/keras/blob/master/examples/mnist_ cnn.py. After training the model, use the outputs from the top level feature layer before the softmax classifier
# Why use Keras? There are countless deep learning frameworks available today. Why use Keras rather than any other? Here are some of the areas in which Keras compares favorably to existing alternatives. — ## Keras prioritizes developer experience – Keras is an API designed for human beings, not machines. [Keras follows best practices for
# Keras backends ## What is a “backend”? Keras is a model-level library, providing high-level building blocks for developing deep learning models. It does not handle itself low-level operations such as tensor products, convolutions and so on. Instead, it relies on a specialized, well-optimized tensor manipulation library to do so, serving as the “backend engine”
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## text_to_word_sequence “`python keras.preprocessing.text.text_to_word_sequence(text, filters=’!”#$%&()*+,-./:;?@[\\]^_`{|}~\t\n’, lower=True, split=” “) “` Split a sentence into a list of words. – __Return__: List of words (str). – __Arguments__: – __text__: str. – __filters__: list (or concatenation) of characters to filter out, such as punctuation. Default: ‘!”#$%&()*+,-./:;?@[\\]^_`{|}~\t\n’ , includes basic punctuation, tabs, and newlines. – __lower__: boolean. Whether to
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# Applications Keras Applications are deep learning models that are made available alongside pre-trained weights. These models can be used for prediction, feature extraction, and fine-tuning. Weights are downloaded automatically when instantiating a model. They are stored at `~/.keras/models/`. ## Available models ### Models for image classification with weights trained on ImageNet: – [Xception](#xception) –
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## Usage of regularizers Regularizers allow to apply penalties on layer parameters or layer activity during optimization. These penalties are incorporated in the loss function that the network optimizes. The penalties are applied on a per-layer basis. The exact API will depend on the layer, but the layers `Dense`, `Conv1D`, `Conv2D` and `Conv3D` have a
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