# Getting started with the Keras functional API The Keras functional API is the way to go for defining complex models, such as multi-output models, directed acyclic graphs, or models with shared layers. This guide assumes that you are already familiar with the `Sequential` model. Let’s start with something simple. —– ## First example: a […]
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## Usage of optimizers An optimizer is one of the two arguments required for compiling a Keras model: “`python from keras import optimizers model = Sequential() model.add(Dense(64, kernel_initializer=’uniform’, input_shape=(10,))) model.add(Activation(‘tanh’)) model.add(Activation(‘softmax’)) sgd = optimizers.SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True) model.compile(loss=’mean_squared_error’, optimizer=sgd) “` You can either instantiate an optimizer before passing it to `model.compile()` , as in the
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## Usage of constraints Functions from the `constraints` module allow setting constraints (eg. non-negativity) on network parameters during optimization. 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 unified API. These layers expose 2 keyword arguments: – `kernel_constraint`
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## Usage of initializers Initializations define the way to set the initial random weights of Keras layers. The keyword arguments used for passing initializers to layers will depend on the layer. Usually it is simply `kernel_initializer` and `bias_initializer`: “`python model.add(Dense(64, kernel_initializer=’random_uniform’, bias_initializer=’zeros’)) “` ## Available initializers The following built-in initializers are available as part of
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svm_cnn_roc-checkpoint In [39]: 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 tqdm import tqdm_notebook from sklearn.model_selection import GridSearchCV, StratifiedKFold, KFold from itertools import cycle from sklearn import svm, datasets from sklearn.model_selection import train_test_split from sklearn.preprocessing import label_binarize from sklearn.multiclass
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Computer Organization and Design: The Hardware/Software Interface In Praise of Computer Organization and Design: The Hardware/ Software Interface, Fifth Edition “Textbook selection is oft en a frustrating act of compromise—pedagogy, content coverage, quality of exposition, level of rigor, cost. Computer Organization and Design is the rare book that hits all the right notes across the
# Wrappers for the Scikit-Learn API You can use `Sequential` Keras models (single-input only) as part of your Scikit-Learn workflow via the wrappers found at `keras.wrappers.scikit_learn.py`. There are two wrappers available: `keras.wrappers.scikit_learn.KerasClassifier(build_fn=None, **sk_params)`, which implements the Scikit-Learn classifier interface, `keras.wrappers.scikit_learn.KerasRegressor(build_fn=None, **sk_params)`, which implements the Scikit-Learn regressor interface. ### Arguments – __build_fn__: callable function or class
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## ImageDataGenerator “`python keras.preprocessing.image.ImageDataGenerator(featurewise_center=False, samplewise_center=False, featurewise_std_normalization=False, samplewise_std_normalization=False, zca_whitening=False, zca_epsilon=1e-6, rotation_range=0., width_shift_range=0., height_shift_range=0., shear_range=0., zoom_range=0., channel_shift_range=0., fill_mode=’nearest’, cval=0., horizontal_flip=False, vertical_flip=False, rescale=None, preprocessing_function=None, data_format=K.image_data_format()) “` Generate batches of tensor image data with real-time data augmentation. The data will be looped over (in batches) indefinitely. – __Arguments__: – __featurewise_center__: Boolean. Set input mean to 0 over the dataset,
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Computer Organization and Design: The Hardware/Software Interface In Praise of Computer Organization and Design: The Hardware/ Software Interface, Fifth Edition “Textbook selection is oft en a frustrating act of compromise—pedagogy, content coverage, quality of exposition, level of rigor, cost. Computer Organization and Design is the rare book that hits all the right notes across the
Computer Organization and Design: The Hardware/Software Interface In Praise of Computer Organization and Design: The Hardware/ Software Interface, Fifth Edition “Textbook selection is oft en a frustrating act of compromise—pedagogy, content coverage, quality of exposition, level of rigor, cost. Computer Organization and Design is the rare book that hits all the right notes across the