目录 TensorFlow高层封装:从入门到喷这本书 0. 写在前面 1. TensorFlow高层封装总览 2. Keras介绍 2.1 Keras基本用法 2.2 Keras高级用法 3. Estimator介绍 3.1 Estimator基本用法 3.2 Estimator自定义模型 3.3 使用数据集(Dataset)作为Estimator输入 4. 总结 TensorFlow高层封装:从入门到喷这本书 0. 写在前面 参考书 <TensorFlow:实战Google深度学习框架>(第…

# 1. 自定义模型并训练. import numpy as np import tensorflow as tf from tensorflow.examples.tutorials.mnist import input_data tf.logging.set_verbosity(tf.logging.INFO) def lenet(x, is_training): x = tf.reshape(x, shape=[-1, 28, 28, 1]) conv1 = tf.layers.conv2…

# 1. 模型定义. import numpy as np import tensorflow as tf from tensorflow.examples.tutorials.mnist import input_data tf.logging.set_verbosity(tf.logging.INFO) mnist = input_data.read_data_sets("F:\\TensorFlowGoogle\\201806-github\\datasets\\MNIST_data&qu…

# 1. 模型定义. import tensorflow as tf from tensorflow.examples.tutorials.mnist import input_data mnist_data = input_data.read_data_sets('F:\\TensorFlowGoogle\\201806-github\\datasets\\MNIST_data', one_hot=True) # 通过TensorFlow中的placeholder定义输入. x = tf.pl…

# 1. 数据预处理. import keras from keras.models import Model from keras.datasets import mnist from keras.layers import Input, Dense from tflearn.layers.core import fully_connected num_classes = 10 img_rows, img_cols = 28, 28 # 通过Keras封装好的API加载MNIST数据. (tr…

# 1. 数据预处理. import keras from keras.models import Model from keras.datasets import mnist from keras.layers import Input, Dense from tflearn.layers.core import fully_connected num_classes = 10 img_rows, img_cols = 28, 28 # 通过Keras封装好的API加载MNIST数据. (tr…

# 1. 数据预处理 import keras from keras import backend as K from keras.datasets import mnist from keras.models import Sequential from keras.layers import Dense, Flatten, Conv2D, MaxPooling2D num_classes = 10 img_rows, img_cols = 28, 28 # 通过Keras封装好的API加载M…

# 1. 通过TensorFlow-Slim定义卷机神经网络 import numpy as np import tensorflow as tf import tensorflow.contrib.slim as slim from tensorflow.examples.tutorials.mnist import input_data # 通过TensorFlow-Slim来定义LeNet-5的网络结构. def lenet5(inputs): inputs = tf.reshape(in…

# 1. 数据预处理. from keras.layers import LSTM from keras.datasets import imdb from keras.models import Sequential from keras.preprocessing import sequence from keras.layers import Dense, Embedding max_features = 20000 maxlen = 80 batch_size = 32 # 加载数据并将…

将原来版本的keras卸载了,再安装2.1.5版本的keras就可以了.…