Keras与TensorFlow深度学习实战指南

Learning
Tensorflow
keras
tensorflow
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资源摘要信息:"本书《Deep Learning with Keras and TensorFlow》深入探讨了基于Python的深度学习技术，以Keras和TensorFlow为工具进行教学。Keras是一个高层神经网络API，运行在TensorFlow、CNTK或Theano之上，能够以TensorFlow作为后端运行深度学习模型。TensorFlow是由Google开发的一个开源机器学习框架，提供了完整的工具和库来构建和部署机器学习模型。在本书中，读者将学习到深度学习的基础知识，包括神经网络的构建、训练和优化，以及如何使用Keras和TensorFlow来实现这些功能。 书中将涵盖以下几个方面： 1. 深度学习基础：解释神经网络的工作原理，包括前馈网络、卷积神经网络（CNN）、循环神经网络（RNN）和长短期记忆网络（LSTM）等不同类型的网络结构。 2. Keras入门：介绍如何使用Keras构建模型，包括序列模型和函数式API，以及如何定义网络层、激活函数和损失函数。 3. TensorFlow核心概念：详细解读TensorFlow的工作机制，包括计算图、张量操作、变量管理和会话（Session）的使用。 4. 模型训练与评估：讲解如何在Keras和TensorFlow中训练模型，包括选择优化器、损失函数、评估指标和性能调优。 5. 深度学习项目实战：通过实际的案例学习，如图像识别、文本分类和生成对抗网络（GANs），将理论知识转化为实际应用。 6. 迁移学习和模型部署：指导如何使用Keras和TensorFlow进行模型的迁移学习，以及如何将训练好的模型部署到生产环境中。 本书适合对深度学习感兴趣的读者，无论他们是初学者还是希望提高自己深度学习技能的专业人士。通过阅读本书，读者将获得将Keras和TensorFlow应用于复杂问题解决的实际经验和知识。" 以上内容基于文件提供的信息编写，详细解释了标题中提到的"Deep Learning with Keras and TensorFlow"一书的核心内容，同时包括了对描述中提及的深度学习基础、Keras和TensorFlow的使用、模型训练与评估以及深度学习项目实战的知识点的说明，并提供了标签中所涉及的"Learning Tensorflow keras tensorflow"学习路径的概述。

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• Building and training neural nets using TensorFlow. • The most important neural net architectures: feedforward neural nets, convolutional nets, recurrent nets, long short-term memory (LSTM) nets, ...

逐行详细解释以下代码并加注释from tensorflow import keras import matplotlib.pyplot as plt base_image_path = keras.utils.get_file( "coast.jpg", origin="https://img-datasets.s3.amazonaws.com/coast.jpg") plt.axis("off") plt.imshow(keras.utils.load_img(base_image_path)) #instantiating a model from tensorflow.keras.applications import inception_v3 model = inception_v3.InceptionV3(weights='imagenet',include_top=False) #配置各层对DeepDream损失的贡献 layer_settings = { "mixed4": 1.0, "mixed5": 1.5, "mixed6": 2.0, "mixed7": 2.5, } outputs_dict = dict( [ (layer.name, layer.output) for layer in [model.get_layer(name) for name in layer_settings.keys()] ] ) feature_extractor = keras.Model(inputs=model.inputs, outputs=outputs_dict) #定义损失函数 import tensorflow as tf def compute_loss(input_image): features = feature_extractor(input_image) loss = tf.zeros(shape=()) for name in features.keys(): coeff = layer_settings[name] activation = features[name] loss += coeff * tf.reduce_mean(tf.square(activation[:, 2:-2, 2:-2, :])) return loss #梯度上升过程 @tf.function def gradient_ascent_step(image, learning_rate): with tf.GradientTape() as tape: tape.watch(image) loss = compute_loss(image) grads = tape.gradient(loss, image) grads = tf.math.l2_normalize(grads) image += learning_rate * grads return loss, image def gradient_ascent_loop(image, iterations, learning_rate, max_loss=None): for i in range(iterations): loss, image = gradient_ascent_step(image, learning_rate) if max_loss is not None and loss > max_loss: break print(f"... Loss value at step {i}: {loss:.2f}") return image #hyperparameters step = 20. num_octave = 3 octave_scale = 1.4 iterations = 30 max_loss = 15. #图像处理方面 import numpy as np def preprocess_image(image_path): img = keras.utils.load_img(image_path) img = keras.utils.img_to_array(img) img = np.expand_dims(img, axis=0) img = keras.applications.inception_v3.preprocess_input(img) return img def deprocess_image(img): img = img.reshape((img.shape[1], img.shape[2], 3)) img /= 2.0 img += 0.5 img *= 255. img = np.clip(img, 0, 255).astype("uint8") return img #在多个连续 上运行梯度上升 original_img = preprocess_image(base_image_path) original_shape = original_img.shape[1:3] successive_shapes = [original_shape] for i in range(1, num_octave): shape = tuple([int(dim / (octave_scale ** i)) for dim in original_shape]) successive_shapes.append(shape) successive_shapes = successive_shapes[::-1] shrunk_original_img = tf.image.resize(original_img, successive_shapes[0]) img = tf.identity(original_img) for i, shape in enumerate(successive_shapes): print(f"Processing octave {i} with shape {shape}") img = tf.image.resize(img, shape) img = gradient_ascent_loop( img, iterations=iterations, learning_rate=step, max_loss=max_loss ) upscaled_shrunk_original_img = tf.image.resize(shrunk_original_img, shape) same_size_original = tf.image.resize(original_img, shape) lost_detail = same_size_original - upscaled_shrunk_original_img img += lost_detail shrunk_original_img = tf.image.resize(original_img, shape) keras.utils.save_img("DeepDream.png", deprocess_image(img.numpy()))

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from tensorflow import keras import matplotlib.pyplot as plt ``` 导入了 TensorFlow 和 Keras 库，以及用于绘制图像的 Matplotlib 库。 2. 加载图像 ```python base_image_path = keras.utils.get_file( ...

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