text stringlengths 5 261k | id stringlengths 16 106 | metadata dict | __index_level_0__ int64 0 266 |
|---|---|---|---|
sudo pip install --upgrade pip
sudo pip install -e ".[tests]"
echo "sh shell/lint.sh" > .git/hooks/pre-commit
chmod a+x .git/hooks/pre-commit | autokeras/.devcontainer/setup.sh/0 | {
"file_path": "autokeras/.devcontainer/setup.sh",
"repo_id": "autokeras",
"token_count": 53
} | 0 |
# Copyright 2020 The AutoKeras Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to i... | autokeras/autokeras/blocks/wrapper.py/0 | {
"file_path": "autokeras/autokeras/blocks/wrapper.py",
"repo_id": "autokeras",
"token_count": 4560
} | 1 |
# Copyright 2020 The AutoKeras Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to i... | autokeras/autokeras/engine/preprocessor.py/0 | {
"file_path": "autokeras/autokeras/engine/preprocessor.py",
"repo_id": "autokeras",
"token_count": 649
} | 2 |
# Copyright 2020 The AutoKeras Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to i... | autokeras/autokeras/pipeline.py/0 | {
"file_path": "autokeras/autokeras/pipeline.py",
"repo_id": "autokeras",
"token_count": 3266
} | 3 |
# Copyright 2020 The AutoKeras Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to i... | autokeras/autokeras/tuners/greedy.py/0 | {
"file_path": "autokeras/autokeras/tuners/greedy.py",
"repo_id": "autokeras",
"token_count": 3643
} | 4 |
# Copyright 2020 The AutoKeras Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to i... | autokeras/autokeras/utils/utils.py/0 | {
"file_path": "autokeras/autokeras/utils/utils.py",
"repo_id": "autokeras",
"token_count": 2354
} | 5 |
FROM python:3.7
RUN pip install flake8 black isort
WORKDIR /autokeras
CMD ["python", "docker/pre_commit.py"]
| autokeras/docker/pre-commit.Dockerfile/0 | {
"file_path": "autokeras/docker/pre-commit.Dockerfile",
"repo_id": "autokeras",
"token_count": 43
} | 6 |
import pathlib
import shutil
from inspect import getdoc
from inspect import isclass
from typing import Dict
from typing import List
from typing import Union
from typing import get_type_hints
from . import utils
from .docstring import process_docstring
from .examples import copy_examples
from .get_signatures import get... | autokeras/docs/keras_autodoc/autogen.py/0 | {
"file_path": "autokeras/docs/keras_autodoc/autogen.py",
"repo_id": "autokeras",
"token_count": 2855
} | 7 |
"""shell
pip install autokeras
"""
import os
import numpy as np
import tensorflow as tf
from sklearn.datasets import load_files
import autokeras as ak
"""
To make this tutorial easy to follow, we just treat IMDB dataset as a
regression dataset. It means we will treat prediction targets of IMDB dataset,
which are 0s ... | autokeras/docs/py/text_regression.py/0 | {
"file_path": "autokeras/docs/py/text_regression.py",
"repo_id": "autokeras",
"token_count": 1992
} | 8 |
:root>* {
--md-primary-fg-color: #d00000;
--md-accent-fg-color: #d00000;
} | autokeras/docs/templates/stylesheets/extra.css/0 | {
"file_path": "autokeras/docs/templates/stylesheets/extra.css",
"repo_id": "autokeras",
"token_count": 38
} | 9 |
from distutils.core import setup
from pathlib import Path
from setuptools import find_packages
this_file = Path(__file__).resolve()
readme = this_file.parent / "README.md"
setup(
name="autokeras",
description="AutoML for deep learning",
package_data={"": ["README.md"]},
long_description=readme.read_t... | autokeras/setup.py/0 | {
"file_path": "autokeras/setup.py",
"repo_id": "autokeras",
"token_count": 771
} | 10 |
# keras-cv Single Stage Two-Dimensional Object Detection API
| Status | Proposed |
:-------------- |:---------------------------------------------------- |
| **Author(s)** | Zhenyu Tan (tanzheny@google.com), Francois Chollet (fchollet@google.com)|
| **Contributor(s)** | Pengchong Jin (pengchong@google.com)... | governance/rfcs/20200928-keras-cv-single-stage-2d-object-detection.md/0 | {
"file_path": "governance/rfcs/20200928-keras-cv-single-stage-2d-object-detection.md",
"repo_id": "governance",
"token_count": 6025
} | 11 |
sudo: required
dist: trusty
language: python
matrix:
include:
- python: 2.7
env: KERAS_BACKEND=tensorflow TEST_MODE=PEP8
- python: 2.7
env: KERAS_BACKEND=tensorflow
- python: 2.7
env: KERAS_BACKEND=tensorflow KERAS_HEAD=true
- python: 3.6
env: ... | keras-applications/.travis.yml/0 | {
"file_path": "keras-applications/.travis.yml",
"repo_id": "keras-applications",
"token_count": 1665
} | 12 |
"""ResNet models for Keras.
# Reference paper
- [Deep Residual Learning for Image Recognition]
(https://arxiv.org/abs/1512.03385) (CVPR 2016 Best Paper Award)
# Reference implementations
- [TensorNets]
(https://github.com/taehoonlee/tensornets/blob/master/tensornets/resnets.py)
- [Caffe ResNet]
(https://githu... | keras-applications/keras_applications/resnet.py/0 | {
"file_path": "keras-applications/keras_applications/resnet.py",
"repo_id": "keras-applications",
"token_count": 363
} | 13 |
# keras-contrib : Keras community contributions
Keras-contrib is deprecated. Use [TensorFlow Addons](https://github.com/tensorflow/addons).
## The future of Keras-contrib:
We're migrating to [tensorflow/addons](https://github.com/tensorflow/addons). See the announcement [here](https://github.com/keras-team/keras-con... | keras-contrib/README.md/0 | {
"file_path": "keras-contrib/README.md",
"repo_id": "keras-contrib",
"token_count": 1072
} | 14 |
{%- for toc_item in toc_item.children %}
<li class="toctree-l{{ navlevel}}"><a class="reference internal" href="{% if not nav_item == page %}{{ nav_item.url|url }}{% endif %}{{ toc_item.url }}">{{ toc_item.title }}</a>
{%- set navlevel = navlevel + 1 %}
{%- if navlevel <= config.theme.navigation_depth and ... | keras-contrib/contrib_docs/theme/toc.html/0 | {
"file_path": "keras-contrib/contrib_docs/theme/toc.html",
"repo_id": "keras-contrib",
"token_count": 215
} | 15 |
from .densenet import DenseNet
from .resnet import ResNet, ResNet18, ResNet34, ResNet50, ResNet101, ResNet152
from .wide_resnet import WideResidualNetwork
from .nasnet import NASNet, NASNetLarge, NASNetMobile
| keras-contrib/keras_contrib/applications/__init__.py/0 | {
"file_path": "keras-contrib/keras_contrib/applications/__init__.py",
"repo_id": "keras-contrib",
"token_count": 67
} | 16 |
from __future__ import absolute_import
from keras import backend as K
from keras.constraints import Constraint
class Clip(Constraint):
"""Clips weights to [-c, c].
# Arguments
c: Clipping parameter.
"""
def __init__(self, c=0.01):
self.c = c
def __call__(self, p):
return... | keras-contrib/keras_contrib/constraints/clip.py/0 | {
"file_path": "keras-contrib/keras_contrib/constraints/clip.py",
"repo_id": "keras-contrib",
"token_count": 202
} | 17 |
# -*- coding: utf-8 -*-
from __future__ import absolute_import
from keras.layers import Layer
from keras_contrib import backend as KC
from keras_contrib.utils.conv_utils import normalize_data_format
class SubPixelUpscaling(Layer):
""" Sub-pixel convolutional upscaling layer.
This layer requires a Convoluti... | keras-contrib/keras_contrib/layers/convolutional/subpixelupscaling.py/0 | {
"file_path": "keras-contrib/keras_contrib/layers/convolutional/subpixelupscaling.py",
"repo_id": "keras-contrib",
"token_count": 1456
} | 18 |
from keras import backend as K
from keras.optimizers import Optimizer
class Yogi(Optimizer):
"""Yogi optimizer.
Yogi is a variation of Adam that controls the increase in effective
learning rate, which (according to the paper) leads to even better
performance than Adam with similar theoretical guarante... | keras-contrib/keras_contrib/optimizers/yogi.py/0 | {
"file_path": "keras-contrib/keras_contrib/optimizers/yogi.py",
"repo_id": "keras-contrib",
"token_count": 1858
} | 19 |
import numpy as np
import pytest
from keras import backend as K
from keras.layers import Input
from keras.models import Sequential, Model
from numpy.testing import assert_allclose
from keras_contrib.layers import InstanceNormalization
from keras_contrib.utils.test_utils import layer_test
input_1 = np.arange(10)
input... | keras-contrib/tests/keras_contrib/layers/normalization/test_instancenormalization.py/0 | {
"file_path": "keras-contrib/tests/keras_contrib/layers/normalization/test_instancenormalization.py",
"repo_id": "keras-contrib",
"token_count": 3078
} | 20 |
from markdown import markdown
from docs import autogen
import pytest
test_doc1 = {
'doc': """Base class for recurrent layers.
# Arguments
cell: A RNN cell instance. A RNN cell is a class that has:
- a `call(input_at_t, states_at_t)` method, returning
`(output_at_t, states_a... | keras-contrib/tests/tooling/test_doc_auto_generation.py/0 | {
"file_path": "keras-contrib/tests/tooling/test_doc_auto_generation.py",
"repo_id": "keras-contrib",
"token_count": 5533
} | 21 |
# Keras Core is becoming Keras 3 and has moved to keras-team/keras
Multi-backend Keras has a new repo: [keras-team/keras](https://github.com/keras-team/keras).
Open any issues / PRs there. `keras-team/keras-core` is no longer in use.
**Keras Core** was the codename of the multi-backend Keras project throughout its in... | keras-core/README.md/0 | {
"file_path": "keras-core/README.md",
"repo_id": "keras-core",
"token_count": 331
} | 22 |
import time
import keras_core
class BenchmarkMetricsCallback(keras_core.callbacks.Callback):
def __init__(self, start_batch=1, stop_batch=None):
self.start_batch = start_batch
self.stop_batch = stop_batch
# Store the throughput of each epoch.
self.state = {"throughput": []}
... | keras-core/benchmarks/model_benchmark/benchmark_utils.py/0 | {
"file_path": "keras-core/benchmarks/model_benchmark/benchmark_utils.py",
"repo_id": "keras-core",
"token_count": 348
} | 23 |
import numpy as np
import keras_core
from keras_core import layers
from keras_core.utils import to_categorical
# Model / data parameters
num_classes = 10
input_shape = (28, 28, 1)
# Load the data and split it between train and test sets
(x_train, y_train), (x_test, y_test) = keras_core.datasets.mnist.load_data()
# S... | keras-core/examples/demo_mnist_convnet.py/0 | {
"file_path": "keras-core/examples/demo_mnist_convnet.py",
"repo_id": "keras-core",
"token_count": 645
} | 24 |
"""
Title: English speaker accent recognition using Transfer Learning
Author: [Fadi Badine](https://twitter.com/fadibadine)
Converted to Keras Core by: [Fadi Badine](https://twitter.com/fadibadine)
Date created: 2022/04/16
Last modified: 2023/07/19
Description: Training a model to classify UK & Ireland accents using fe... | keras-core/examples/keras_io/tensorflow/audio/uk_ireland_accent_recognition.py/0 | {
"file_path": "keras-core/examples/keras_io/tensorflow/audio/uk_ireland_accent_recognition.py",
"repo_id": "keras-core",
"token_count": 7353
} | 25 |
"""
Title: End-to-end Masked Language Modeling with BERT
Author: [Ankur Singh](https://twitter.com/ankur310794)
Converted to Keras-Core: [Mrutyunjay Biswal](https://twitter.com/LearnStochastic)
Date created: 2020/09/18
Last modified: 2023/09/06
Description: Implement a Masked Language Model (MLM) with BERT and fine-tun... | keras-core/examples/keras_io/tensorflow/nlp/end_to_end_mlm_with_bert.py/0 | {
"file_path": "keras-core/examples/keras_io/tensorflow/nlp/end_to_end_mlm_with_bert.py",
"repo_id": "keras-core",
"token_count": 6779
} | 26 |
"""
Title: Zero-DCE for low-light image enhancement
Author: [Soumik Rakshit](http://github.com/soumik12345)
Converted to Keras Core by: [Soumik Rakshit](http://github.com/soumik12345)
Date created: 2021/09/18
Last modified: 2023/07/15
Description: Implementing Zero-Reference Deep Curve Estimation for low-light image en... | keras-core/examples/keras_io/tensorflow/vision/zero_dce.py/0 | {
"file_path": "keras-core/examples/keras_io/tensorflow/vision/zero_dce.py",
"repo_id": "keras-core",
"token_count": 7947
} | 27 |
"""
Title: Training & evaluation with the built-in methods
Author: [fchollet](https://twitter.com/fchollet)
Date created: 2019/03/01
Last modified: 2023/03/25
Description: Complete guide to training & evaluation with `fit()` and `evaluate()`.
Accelerator: GPU
"""
"""
## Setup
"""
# We import torch & TF so as to use t... | keras-core/guides/training_with_built_in_methods.py/0 | {
"file_path": "keras-core/guides/training_with_built_in_methods.py",
"repo_id": "keras-core",
"token_count": 13375
} | 28 |
try:
import namex
except ImportError:
namex = None
# These dicts reference "canonical names" only
# (i.e. the first name an object was registered with).
REGISTERED_NAMES_TO_OBJS = {}
REGISTERED_OBJS_TO_NAMES = {}
def register_internal_serializable(path, symbol):
global REGISTERED_NAMES_TO_OBJS
if is... | keras-core/keras_core/api_export.py/0 | {
"file_path": "keras-core/keras_core/api_export.py",
"repo_id": "keras-core",
"token_count": 511
} | 29 |
from keras_core.api_export import keras_core_export
from keras_core.applications import imagenet_utils
from keras_core.applications import resnet
@keras_core_export(
[
"keras_core.applications.ResNet50V2",
"keras_core.applications.resnet_v2.ResNet50V2",
]
)
def ResNet50V2(
include_top=True... | keras-core/keras_core/applications/resnet_v2.py/0 | {
"file_path": "keras-core/keras_core/applications/resnet_v2.py",
"repo_id": "keras-core",
"token_count": 2778
} | 30 |
import numpy as np
from keras_core import backend
from keras_core import ops
from keras_core import testing
from keras_core.backend.common.stateless_scope import StatelessScope
class TestStatelessScope(testing.TestCase):
def test_basic_flow(self):
var1 = backend.Variable(np.zeros((2,)))
var2 = ba... | keras-core/keras_core/backend/common/stateless_scope_test.py/0 | {
"file_path": "keras-core/keras_core/backend/common/stateless_scope_test.py",
"repo_id": "keras-core",
"token_count": 900
} | 31 |
from keras_core.backend.numpy import core
from keras_core.backend.numpy import image
from keras_core.backend.numpy import math
from keras_core.backend.numpy import nn
from keras_core.backend.numpy import numpy
from keras_core.backend.numpy import random
from keras_core.backend.numpy.core import SUPPORTS_SPARSE_TENSORS
... | keras-core/keras_core/backend/numpy/__init__.py/0 | {
"file_path": "keras-core/keras_core/backend/numpy/__init__.py",
"repo_id": "keras-core",
"token_count": 351
} | 32 |
import tensorflow as tf
from keras_core.backend.tensorflow.core import name_scope
from keras_core.testing import TestCase
class TFNameScopeTest(TestCase):
def test_stacking(self):
self.assertEqual(tf.Variable(0, name="x").name, "x:0")
with name_scope("outer") as outer:
self.assertEqua... | keras-core/keras_core/backend/tensorflow/name_scope_test.py/0 | {
"file_path": "keras-core/keras_core/backend/tensorflow/name_scope_test.py",
"repo_id": "keras-core",
"token_count": 842
} | 33 |
import numpy as np
import torch
import torch.nn.functional as tnn
from keras_core.backend import standardize_data_format
from keras_core.backend import standardize_dtype
from keras_core.backend.common.backend_utils import (
compute_conv_transpose_padding_args_for_torch,
)
from keras_core.backend.config import epsi... | keras-core/keras_core/backend/torch/nn.py/0 | {
"file_path": "keras-core/keras_core/backend/torch/nn.py",
"repo_id": "keras-core",
"token_count": 9280
} | 34 |
"""Boston housing price regression dataset."""
import numpy as np
from keras_core.api_export import keras_core_export
from keras_core.utils.file_utils import get_file
@keras_core_export("keras_core.datasets.boston_housing.load_data")
def load_data(path="california_housing.npz", test_split=0.2, seed=113):
"""Loa... | keras-core/keras_core/datasets/california_housing.py/0 | {
"file_path": "keras-core/keras_core/datasets/california_housing.py",
"repo_id": "keras-core",
"token_count": 1232
} | 35 |
import numpy as np
from keras_core import backend
from keras_core import initializers
from keras_core import testing
class ConstantInitializersTest(testing.TestCase):
def test_zeros_initializer(self):
shape = (3, 3)
initializer = initializers.Zeros()
values = initializer(shape=shape)
... | keras-core/keras_core/initializers/constant_initializers_test.py/0 | {
"file_path": "keras-core/keras_core/initializers/constant_initializers_test.py",
"repo_id": "keras-core",
"token_count": 737
} | 36 |
from keras_core import activations
from keras_core import backend
from keras_core.api_export import keras_core_export
from keras_core.layers.layer import Layer
def _large_negative_number(dtype):
"""Return a Large negative number based on dtype."""
if backend.standardize_dtype(dtype) == "float16":
retu... | keras-core/keras_core/layers/activations/softmax.py/0 | {
"file_path": "keras-core/keras_core/layers/activations/softmax.py",
"repo_id": "keras-core",
"token_count": 1039
} | 37 |
import pytest
from absl.testing import parameterized
from keras_core import layers
from keras_core import testing
class EinsumDenseTest(testing.TestCase, parameterized.TestCase):
@parameterized.named_parameters(
{
"testcase_name": "_1d_end_weight",
"equation": "ab,b->a",
... | keras-core/keras_core/layers/core/einsum_dense_test.py/0 | {
"file_path": "keras-core/keras_core/layers/core/einsum_dense_test.py",
"repo_id": "keras-core",
"token_count": 5390
} | 38 |
from keras_core import constraints
from keras_core import initializers
from keras_core import ops
from keras_core import regularizers
from keras_core.api_export import keras_core_export
from keras_core.layers.layer import Layer
@keras_core_export("keras_core.layers.LayerNormalization")
class LayerNormalization(Layer)... | keras-core/keras_core/layers/normalization/layer_normalization.py/0 | {
"file_path": "keras-core/keras_core/layers/normalization/layer_normalization.py",
"repo_id": "keras-core",
"token_count": 4134
} | 39 |
import numpy as np
import pytest
from absl.testing import parameterized
from keras_core import layers
from keras_core import testing
@pytest.mark.requires_trainable_backend
class GlobalAveragePoolingBasicTest(testing.TestCase, parameterized.TestCase):
@parameterized.parameters(
("channels_last", False, (... | keras-core/keras_core/layers/pooling/global_average_pooling_test.py/0 | {
"file_path": "keras-core/keras_core/layers/pooling/global_average_pooling_test.py",
"repo_id": "keras-core",
"token_count": 3088
} | 40 |
import tree
from keras_core import backend
from keras_core import layers
from keras_core.api_export import keras_core_export
from keras_core.layers.layer import Layer
from keras_core.saving import saving_lib
from keras_core.saving import serialization_lib
from keras_core.utils import backend_utils
from keras_core.util... | keras-core/keras_core/layers/preprocessing/feature_space.py/0 | {
"file_path": "keras-core/keras_core/layers/preprocessing/feature_space.py",
"repo_id": "keras-core",
"token_count": 13723
} | 41 |
from keras_core import backend
from keras_core.api_export import keras_core_export
from keras_core.layers.preprocessing.tf_data_layer import TFDataLayer
from keras_core.random.seed_generator import SeedGenerator
from keras_core.utils import image_utils
@keras_core_export("keras_core.layers.RandomCrop")
class RandomCr... | keras-core/keras_core/layers/preprocessing/random_crop.py/0 | {
"file_path": "keras-core/keras_core/layers/preprocessing/random_crop.py",
"repo_id": "keras-core",
"token_count": 3120
} | 42 |
import numpy as np
from keras_core import backend
from keras_core.api_export import keras_core_export
from keras_core.layers.layer import Layer
from keras_core.layers.preprocessing.index_lookup import listify_tensors
from keras_core.layers.preprocessing.string_lookup import StringLookup
from keras_core.saving import s... | keras-core/keras_core/layers/preprocessing/text_vectorization.py/0 | {
"file_path": "keras-core/keras_core/layers/preprocessing/text_vectorization.py",
"repo_id": "keras-core",
"token_count": 11797
} | 43 |
import numpy as np
import pytest
from keras_core import layers
from keras_core import ops
from keras_core import testing
class Cropping1DTest(testing.TestCase):
@pytest.mark.requires_trainable_backend
def test_cropping_1d(self):
inputs = np.random.rand(3, 5, 7)
# Cropping with different valu... | keras-core/keras_core/layers/reshaping/cropping1d_test.py/0 | {
"file_path": "keras-core/keras_core/layers/reshaping/cropping1d_test.py",
"repo_id": "keras-core",
"token_count": 1209
} | 44 |
# flake8: noqa
import numpy as np
import pytest
from absl.testing import parameterized
from keras_core import backend
from keras_core import layers
from keras_core import testing
class UpSampling2dTest(testing.TestCase, parameterized.TestCase):
@parameterized.product(
data_format=["channels_first", "chan... | keras-core/keras_core/layers/reshaping/up_sampling2d_test.py/0 | {
"file_path": "keras-core/keras_core/layers/reshaping/up_sampling2d_test.py",
"repo_id": "keras-core",
"token_count": 2511
} | 45 |
import numpy as np
import pytest
from keras_core import initializers
from keras_core import layers
from keras_core import testing
class ConvLSTM2DTest(testing.TestCase):
@pytest.mark.requires_trainable_backend
def test_basics(self):
self.run_layer_test(
layers.ConvLSTM2D,
init... | keras-core/keras_core/layers/rnn/conv_lstm2d_test.py/0 | {
"file_path": "keras-core/keras_core/layers/rnn/conv_lstm2d_test.py",
"repo_id": "keras-core",
"token_count": 1510
} | 46 |
"""Wrapper layer to apply every temporal slice of an input."""
from keras_core import backend
from keras_core import ops
from keras_core.api_export import keras_core_export
from keras_core.layers.core.wrapper import Wrapper
from keras_core.layers.layer import Layer
@keras_core_export("keras_core.layers.TimeDistribut... | keras-core/keras_core/layers/rnn/time_distributed.py/0 | {
"file_path": "keras-core/keras_core/layers/rnn/time_distributed.py",
"repo_id": "keras-core",
"token_count": 1949
} | 47 |
import json
import threading
import tree
from absl import logging
from keras_core import backend
from keras_core import layers
from keras_core import losses
from keras_core import metrics as metrics_module
from keras_core import models
from keras_core import optimizers
from keras_core.legacy.saving import serializati... | keras-core/keras_core/legacy/saving/saving_utils.py/0 | {
"file_path": "keras-core/keras_core/legacy/saving/saving_utils.py",
"repo_id": "keras-core",
"token_count": 3741
} | 48 |
from keras_core import backend
from keras_core import initializers
from keras_core import ops
from keras_core.api_export import keras_core_export
from keras_core.metrics.metric import Metric
from keras_core.metrics.metrics_utils import confusion_matrix
class _IoUBase(Metric):
"""Computes the confusion matrix for ... | keras-core/keras_core/metrics/iou_metrics.py/0 | {
"file_path": "keras-core/keras_core/metrics/iou_metrics.py",
"repo_id": "keras-core",
"token_count": 11729
} | 49 |
import copy
import inspect
import warnings
import tree
from keras_core import backend
from keras_core import ops
from keras_core.backend.common import global_state
from keras_core.layers.input_spec import InputSpec
from keras_core.layers.layer import Layer
from keras_core.legacy.saving import saving_utils
from keras_... | keras-core/keras_core/models/functional.py/0 | {
"file_path": "keras-core/keras_core/models/functional.py",
"repo_id": "keras-core",
"token_count": 12279
} | 50 |
import math
import numpy as np
import pytest
import scipy.signal
from absl.testing import parameterized
from keras_core import backend
from keras_core import testing
from keras_core.backend.common.keras_tensor import KerasTensor
from keras_core.ops import math as kmath
def _stft(
x, sequence_length, sequence_st... | keras-core/keras_core/ops/math_test.py/0 | {
"file_path": "keras-core/keras_core/ops/math_test.py",
"repo_id": "keras-core",
"token_count": 15659
} | 51 |
from keras_core import backend
from keras_core import ops
from keras_core.api_export import keras_core_export
from keras_core.optimizers import optimizer
@keras_core_export(["keras_core.optimizers.Adafactor"])
class Adafactor(optimizer.Optimizer):
"""Optimizer that implements the Adafactor algorithm.
Adafact... | keras-core/keras_core/optimizers/adafactor.py/0 | {
"file_path": "keras-core/keras_core/optimizers/adafactor.py",
"repo_id": "keras-core",
"token_count": 3683
} | 52 |
import numpy as np
from absl.testing import parameterized
from keras_core import backend
from keras_core import ops
from keras_core import testing
from keras_core.optimizers.loss_scale_optimizer import LossScaleOptimizer
from keras_core.optimizers.sgd import SGD
class LossScaleOptimizerTest(testing.TestCase, paramet... | keras-core/keras_core/optimizers/loss_scale_optimizer_test.py/0 | {
"file_path": "keras-core/keras_core/optimizers/loss_scale_optimizer_test.py",
"repo_id": "keras-core",
"token_count": 2047
} | 53 |
import inspect
from keras_core.api_export import keras_core_export
from keras_core.regularizers.regularizers import L1
from keras_core.regularizers.regularizers import L1L2
from keras_core.regularizers.regularizers import L2
from keras_core.regularizers.regularizers import OrthogonalRegularizer
from keras_core.regular... | keras-core/keras_core/regularizers/__init__.py/0 | {
"file_path": "keras-core/keras_core/regularizers/__init__.py",
"repo_id": "keras-core",
"token_count": 709
} | 54 |
import tree
from keras_core import backend
from keras_core import losses as losses_module
from keras_core import metrics as metrics_module
from keras_core import ops
from keras_core.utils.naming import get_object_name
class MetricsList(metrics_module.Metric):
def __init__(self, metrics, name="metrics_list", outp... | keras-core/keras_core/trainers/compile_utils.py/0 | {
"file_path": "keras-core/keras_core/trainers/compile_utils.py",
"repo_id": "keras-core",
"token_count": 13763
} | 55 |
import numpy as np
import pytest
import tensorflow as tf
from keras_core import backend
from keras_core import testing
from keras_core.trainers import epoch_iterator
class TestEpochIterator(testing.TestCase):
def _test_basic_flow(self, return_type):
x = np.random.random((100, 16))
y = np.random.r... | keras-core/keras_core/trainers/epoch_iterator_test.py/0 | {
"file_path": "keras-core/keras_core/trainers/epoch_iterator_test.py",
"repo_id": "keras-core",
"token_count": 3234
} | 56 |
import numpy as np
from keras_core.api_export import keras_core_export
from keras_core.backend.config import standardize_data_format
from keras_core.utils import dataset_utils
from keras_core.utils import image_utils
from keras_core.utils.module_utils import tensorflow as tf
ALLOWLIST_FORMATS = (".bmp", ".gif", ".jpe... | keras-core/keras_core/utils/image_dataset_utils.py/0 | {
"file_path": "keras-core/keras_core/utils/image_dataset_utils.py",
"repo_id": "keras-core",
"token_count": 6722
} | 57 |
import random
import numpy as np
from keras_core import backend
from keras_core.api_export import keras_core_export
from keras_core.utils.module_utils import tensorflow as tf
@keras_core_export("keras_core.utils.set_random_seed")
def set_random_seed(seed):
"""Sets all random seeds (Python, NumPy, and backend fr... | keras-core/keras_core/utils/rng_utils.py/0 | {
"file_path": "keras-core/keras_core/utils/rng_utils.py",
"repo_id": "keras-core",
"token_count": 566
} | 58 |
# Copyright 2022 The KerasCV Authors
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in ... | keras-cv/benchmarks/vectorization_strategy_benchmark.py/0 | {
"file_path": "keras-cv/benchmarks/vectorization_strategy_benchmark.py",
"repo_id": "keras-cv",
"token_count": 19626
} | 59 |
# Copyright 2023 The KerasCV Authors
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in ... | keras-cv/benchmarks/vectorized_random_translation.py/0 | {
"file_path": "keras-cv/benchmarks/vectorized_random_translation.py",
"repo_id": "keras-cv",
"token_count": 5646
} | 60 |
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIE... | keras-cv/examples/layers/preprocessing/classification/random_zoom_demo.py/0 | {
"file_path": "keras-cv/examples/layers/preprocessing/classification/random_zoom_demo.py",
"repo_id": "keras-cv",
"token_count": 312
} | 61 |
# Copyright 2022 The KerasCV Authors
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in ... | keras-cv/examples/training/classification/imagenet/basic_training.py/0 | {
"file_path": "keras-cv/examples/training/classification/imagenet/basic_training.py",
"repo_id": "keras-cv",
"token_count": 4844
} | 62 |
# Copyright 2023 The KerasCV Authors
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in ... | keras-cv/keras_cv/backend/keras.py/0 | {
"file_path": "keras-cv/keras_cv/backend/keras.py",
"repo_id": "keras-cv",
"token_count": 834
} | 63 |
# Copyright 2022 The KerasCV Authors
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in ... | keras-cv/keras_cv/bounding_box/to_dense.py/0 | {
"file_path": "keras-cv/keras_cv/bounding_box/to_dense.py",
"repo_id": "keras-cv",
"token_count": 1323
} | 64 |
# Copyright 2022 The KerasCV Authors
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in ... | keras-cv/keras_cv/datasets/waymo/struct.py/0 | {
"file_path": "keras-cv/keras_cv/datasets/waymo/struct.py",
"repo_id": "keras-cv",
"token_count": 688
} | 65 |
# Copyright 2022 The KerasCV Authors
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in ... | keras-cv/keras_cv/layers/object_detection/roi_pool.py/0 | {
"file_path": "keras-cv/keras_cv/layers/object_detection/roi_pool.py",
"repo_id": "keras-cv",
"token_count": 3115
} | 66 |
# Copyright 2023 The KerasCV Authors
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in ... | keras-cv/keras_cv/layers/overlapping_patching_embedding.py/0 | {
"file_path": "keras-cv/keras_cv/layers/overlapping_patching_embedding.py",
"repo_id": "keras-cv",
"token_count": 1363
} | 67 |
# Copyright 2022 The KerasCV Authors
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in ... | keras-cv/keras_cv/layers/preprocessing/fourier_mix_test.py/0 | {
"file_path": "keras-cv/keras_cv/layers/preprocessing/fourier_mix_test.py",
"repo_id": "keras-cv",
"token_count": 2606
} | 68 |
# Copyright 2022 The KerasCV Authors
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in ... | keras-cv/keras_cv/layers/preprocessing/random_apply.py/0 | {
"file_path": "keras-cv/keras_cv/layers/preprocessing/random_apply.py",
"repo_id": "keras-cv",
"token_count": 2283
} | 69 |
# Copyright 2023 The KerasCV Authors
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in ... | keras-cv/keras_cv/layers/preprocessing/random_contrast.py/0 | {
"file_path": "keras-cv/keras_cv/layers/preprocessing/random_contrast.py",
"repo_id": "keras-cv",
"token_count": 1804
} | 70 |
# Copyright 2023 The KerasCV Authors
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in ... | keras-cv/keras_cv/layers/preprocessing/random_rotation.py/0 | {
"file_path": "keras-cv/keras_cv/layers/preprocessing/random_rotation.py",
"repo_id": "keras-cv",
"token_count": 5244
} | 71 |
# Copyright 2022 The KerasCV Authors
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in ... | keras-cv/keras_cv/layers/preprocessing/resizing.py/0 | {
"file_path": "keras-cv/keras_cv/layers/preprocessing/resizing.py",
"repo_id": "keras-cv",
"token_count": 7352
} | 72 |
# Copyright 2022 Waymo LLC.
#
# Licensed under the terms in https://github.com/keras-team/keras-cv/blob/master/keras_cv/layers/preprocessing_3d/waymo/LICENSE # noqa: E501
import numpy as np
from keras_cv.layers.preprocessing_3d import base_augmentation_layer_3d
from keras_cv.layers.preprocessing_3d.waymo.frustum_ran... | keras-cv/keras_cv/layers/preprocessing_3d/waymo/frustum_random_dropping_points_test.py/0 | {
"file_path": "keras-cv/keras_cv/layers/preprocessing_3d/waymo/frustum_random_dropping_points_test.py",
"repo_id": "keras-cv",
"token_count": 2383
} | 73 |
# Copyright 2022 Waymo LLC.
#
# Licensed under the terms in https://github.com/keras-team/keras-cv/blob/master/keras_cv/layers/preprocessing_3d/waymo/LICENSE # noqa: E501
import os
import numpy as np
import pytest
from keras_cv.layers.preprocessing_3d import base_augmentation_layer_3d
from keras_cv.layers.preproces... | keras-cv/keras_cv/layers/preprocessing_3d/waymo/random_copy_paste_test.py/0 | {
"file_path": "keras-cv/keras_cv/layers/preprocessing_3d/waymo/random_copy_paste_test.py",
"repo_id": "keras-cv",
"token_count": 5017
} | 74 |
# Copyright 2022 The KerasCV Authors
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in ... | keras-cv/keras_cv/layers/spatial_pyramid.py/0 | {
"file_path": "keras-cv/keras_cv/layers/spatial_pyramid.py",
"repo_id": "keras-cv",
"token_count": 3324
} | 75 |
# Copyright 2022 The KerasCV Authors
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in ... | keras-cv/keras_cv/losses/giou_loss_test.py/0 | {
"file_path": "keras-cv/keras_cv/losses/giou_loss_test.py",
"repo_id": "keras-cv",
"token_count": 1347
} | 76 |
# Copyright 2023 The KerasCV Authors
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in ... | keras-cv/keras_cv/metrics/object_detection/box_coco_metrics_test.py/0 | {
"file_path": "keras-cv/keras_cv/metrics/object_detection/box_coco_metrics_test.py",
"repo_id": "keras-cv",
"token_count": 4825
} | 77 |
# Copyright 2023 The KerasCV Authors
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in ... | keras-cv/keras_cv/models/backbones/densenet/densenet_backbone_presets.py/0 | {
"file_path": "keras-cv/keras_cv/models/backbones/densenet/densenet_backbone_presets.py",
"repo_id": "keras-cv",
"token_count": 1022
} | 78 |
# Copyright 2023 The KerasCV Authors
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in ... | keras-cv/keras_cv/models/backbones/efficientnet_v2/efficientnet_v2_aliases.py/0 | {
"file_path": "keras-cv/keras_cv/models/backbones/efficientnet_v2/efficientnet_v2_aliases.py",
"repo_id": "keras-cv",
"token_count": 4141
} | 79 |
# Copyright 2023 The KerasCV Authors
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in ... | keras-cv/keras_cv/models/backbones/mobilenet_v3/mobilenet_v3_backbone_test.py/0 | {
"file_path": "keras-cv/keras_cv/models/backbones/mobilenet_v3/mobilenet_v3_backbone_test.py",
"repo_id": "keras-cv",
"token_count": 1656
} | 80 |
# Copyright 2023 The KerasCV Authors
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in ... | keras-cv/keras_cv/models/backbones/vit_det/vit_det_aliases.py/0 | {
"file_path": "keras-cv/keras_cv/models/backbones/vit_det/vit_det_aliases.py",
"repo_id": "keras-cv",
"token_count": 1393
} | 81 |
# Copyright 2023 The KerasCV Authors
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in ... | keras-cv/keras_cv/models/feature_extractor/clip/clip_processor.py/0 | {
"file_path": "keras-cv/keras_cv/models/feature_extractor/clip/clip_processor.py",
"repo_id": "keras-cv",
"token_count": 2025
} | 82 |
# Copyright 2022 The KerasCV Authors
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in ... | keras-cv/keras_cv/models/legacy/object_detection/faster_rcnn/faster_rcnn.py/0 | {
"file_path": "keras-cv/keras_cv/models/legacy/object_detection/faster_rcnn/faster_rcnn.py",
"repo_id": "keras-cv",
"token_count": 11857
} | 83 |
# Copyright 2023 The KerasCV Authors
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in ... | keras-cv/keras_cv/models/object_detection/yolo_v8/yolo_v8_label_encoder.py/0 | {
"file_path": "keras-cv/keras_cv/models/object_detection/yolo_v8/yolo_v8_label_encoder.py",
"repo_id": "keras-cv",
"token_count": 5092
} | 84 |
# Copyright 2022 The KerasCV Authors
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in ... | keras-cv/keras_cv/models/stable_diffusion/diffusion_model.py/0 | {
"file_path": "keras-cv/keras_cv/models/stable_diffusion/diffusion_model.py",
"repo_id": "keras-cv",
"token_count": 6703
} | 85 |
# Copyright 2022 The KerasCV Authors
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in ... | keras-cv/keras_cv/point_cloud/point_cloud_test.py/0 | {
"file_path": "keras-cv/keras_cv/point_cloud/point_cloud_test.py",
"repo_id": "keras-cv",
"token_count": 7984
} | 86 |
# Copyright 2022 The KerasCV Authors
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in ... | keras-cv/keras_cv/utils/fill_utils.py/0 | {
"file_path": "keras-cv/keras_cv/utils/fill_utils.py",
"repo_id": "keras-cv",
"token_count": 1152
} | 87 |
# Copyright 2023 The KerasCV Authors
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in ... | keras-cv/shell/count_preset_params.py/0 | {
"file_path": "keras-cv/shell/count_preset_params.py",
"repo_id": "keras-cv",
"token_count": 1060
} | 88 |
<span style="float:right;">[[source]](https://github.com/keras-team/keras/blob/master/keras/layers/advanced_activations.py#L18)</span>
### LeakyReLU
```python
keras.layers.LeakyReLU(alpha=0.3)
```
ユニットがアクティブでないときに微少な勾配を可能とするRectified Linear Unitの特別なバージョン：
`f(x) = alpha * x for x < 0`,
`f(x) = x for x >= 0`.
__入力のsha... | keras-docs-ja/sources/layers/advanced-activations.md/0 | {
"file_path": "keras-docs-ja/sources/layers/advanced-activations.md",
"repo_id": "keras-docs-ja",
"token_count": 2416
} | 89 |
# SequentialモデルAPI
はじめに，[KerasのSequentialモデルのガイド](/getting-started/sequential-model-guide) を参照してください．
## モデルの有用な属性
- `model.layers`は，モデルに加えたレイヤーのリストです．
----
## Sequentialモデルのメソッド
### compile
```python
compile(self, optimizer, loss, metrics=None, sample_weight_mode=None, weighted_metrics=None, target_tensors=Non... | keras-docs-ja/sources/models/sequential.md/0 | {
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"repo_id": "keras-docs-ja",
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} | 90 |
# 케라스 Sequential 모델 시작하기
`Sequential` 모델은 층<sub>layer</sub>을 순서대로 쌓은 것입니다.
아래와 같이 각 층 인스턴스를 리스트 형식으로 나열하여 생성자<sub>constructor</sub>인 `Sequential`로 넘겨주면 모델이 만들어집니다.
```python
from keras.models import Sequential # Sequential 생성자를 불러옵니다.
from keras.layers import Dense, Activation # Dense와 Activation 두... | keras-docs-ko/sources/getting-started/sequential-model-guide.md/0 | {
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## 손실 함수의 사용
손실 함수(목적 함수 또는 최적화 스코어 함수)는 모델을 컴파일하기 위해 필요한 두 개의 매개 변수 중 하나입니다.
```python
model.compile(loss='mean_squared_error', optimizer='sgd')
```
```python
from keras import losses
model.compile(loss=losses.mean_squared_error, optimizer='sgd')
```
케라스가 제공하는 손실 함수의 이름 문자열<sub>string</sub> 또는 TensorFlow/Theano의... | keras-docs-ko/sources/losses.md/0 | {
"file_path": "keras-docs-ko/sources/losses.md",
"repo_id": "keras-docs-ko",
"token_count": 5128
} | 92 |
# 关于 Github Issues 和 Pull Requests
找到一个漏洞？有一个新的功能建议？想要对代码库做出贡献？请务必先阅读这些。
## 漏洞报告
你的代码不起作用，你确定问题在于Keras？请按照以下步骤报告错误。
1. 你的漏洞可能已经被修复了。确保更新到目前的Keras master分支，以及最新的 Theano/TensorFlow/CNTK master 分支。
轻松更新 Theano 的方法：`pip install git+git://github.com/Theano/Theano.git --upgrade`
2. 搜索相似问题。 确保在搜索已经解决的 Issue 时删除 `is:open`... | keras-docs-zh/sources/contributing.md/0 | {
"file_path": "keras-docs-zh/sources/contributing.md",
"repo_id": "keras-docs-zh",
"token_count": 4713
} | 93 |
# 在 IMDB 情绪分类任务上训练循环卷积网络。
2 个轮次后达到 0.8498 的测试精度。K520 GPU 上为 41 秒/轮次。
```python
from __future__ import print_function
from keras.preprocessing import sequence
from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation
from keras.layers import Embedding
from keras.layers import LSTM
from ... | keras-docs-zh/sources/examples/imdb_cnn_lstm.md/0 | {
"file_path": "keras-docs-zh/sources/examples/imdb_cnn_lstm.md",
"repo_id": "keras-docs-zh",
"token_count": 894
} | 94 |
# 训练基于 MNIST 数据集上残差块的堆叠式自动编码器。
它举例说明了过去几年开发的两种有影响力的方法。
首先是适当地 "分拆" 的想法。在任何最大池化期间，都会丢失合并的接收场中最大值的确切位置（where），但是在输入图像的整体重建中可能非常有用。
因此，如果将 "位置" 从编码器传递到相应的解码器层，则可以将要解码的特征 "放置" 在正确的位置，从而可以实现更高保真度的重构。
# 参考文献
- [Visualizing and Understanding Convolutional Networks, Matthew D Zeiler, Rob Fergus](https://arxiv.org/abs/1311.... | keras-docs-zh/sources/examples/mnist_swwae.md/0 | {
"file_path": "keras-docs-zh/sources/examples/mnist_swwae.md",
"repo_id": "keras-docs-zh",
"token_count": 3808
} | 95 |
<span style="float:right;">[[source]](https://github.com/keras-team/keras/blob/master/keras/layers/recurrent.py#L238)</span>
### RNN
```python
keras.layers.RNN(cell, return_sequences=False, return_state=False, go_backwards=False, stateful=False, unroll=False)
```
循环神经网络层基类。
__参数__
- __cell__: 一个 RNN 单元实例。RNN 单元是一个具... | keras-docs-zh/sources/layers/recurrent.md/0 | {
"file_path": "keras-docs-zh/sources/layers/recurrent.md",
"repo_id": "keras-docs-zh",
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} | 96 |
# 为什么选择 Keras？
在如今无数深度学习框架中，为什么要使用 Keras 而非其他？以下是 Keras 与现有替代品的一些比较。
---
## Keras 优先考虑开发人员的经验
- Keras 是为人类而非机器设计的 API。[Keras 遵循减少认知困难的最佳实践](https://blog.keras.io/user-experience-design-for-apis.html): 它提供一致且简单的 API，它将常见用例所需的用户操作数量降至最低，并且在用户错误时提供清晰和可操作的反馈。
- 这使 Keras 易于学习和使用。作为 Keras 用户，你的工作效率更高，能够比竞争对手更快地尝试更多创意，从而... | keras-docs-zh/sources/why-use-keras.md/0 | {
"file_path": "keras-docs-zh/sources/why-use-keras.md",
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"""
Title: MelGAN-based spectrogram inversion using feature matching
Author: [Darshan Deshpande](https://twitter.com/getdarshan)
Date created: 02/09/2021
Last modified: 15/09/2021
Description: Inversion of audio from mel-spectrograms using the MelGAN architecture and feature matching.
Accelerator: GPU
"""
"""
## Intro... | keras-io/examples/audio/melgan_spectrogram_inversion.py/0 | {
"file_path": "keras-io/examples/audio/melgan_spectrogram_inversion.py",
"repo_id": "keras-io",
"token_count": 7617
} | 98 |
"""
Title: GauGAN for conditional image generation
Author: [Soumik Rakshit](https://github.com/soumik12345), [Sayak Paul](https://twitter.com/RisingSayak)
Date created: 2021/12/26
Last modified: 2022/01/03
Description: Implementing a GauGAN for conditional image generation.
Accelerator: GPU
"""
"""
## Introduction
In... | keras-io/examples/generative/gaugan.py/0 | {
"file_path": "keras-io/examples/generative/gaugan.py",
"repo_id": "keras-io",
"token_count": 13074
} | 99 |
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