Keras易于学习的高级Python库，可在TensorFlow框架上运行，它的重点是理解深度学习技术，如为神经网络创建层，以维护形状和数学细节的概念。框架的创建可以分为以下两种类型-
- 顺序API
- 功能API
无涯教程将使用Jupyter Notebook执行和显示输出，如下所示-
步骤1 - 首先执行数据加载和预处理加载的数据以执行深度学习模型。
import warnings
warnings.filterwarnings('ignore')
import numpy as np
np.random.seed(123) # for reproducibility
from keras.models import Sequential
from keras.layers import Flatten, MaxPool2D, Conv2D, Dense, Reshape, Dropout
from keras.utils import np_utils
Using TensorFlow backend.
from keras.datasets import mnist
# 将预混洗的 MNIST 数据加载到训练和测试集中
(X_train, y_train), (X_test, y_test) = mnist.load_data()
X_train = X_train.reshape(X_train.shape[0], 28, 28, 1)
X_test = X_test.reshape(X_test.shape[0], 28, 28, 1)
X_train = X_train.astype('float32')
X_test = X_test.astype('float32')
X_train /= 255
X_test /= 255
Y_train = np_utils.to_categorical(y_train, 10)
Y_test = np_utils.to_categorical(y_test, 10)
可以将该步骤定义为"Import libraries and Modules"，这意味着所有库和模块都将作为初始步骤导入。
步骤2 - 在这一步中，无涯教程将定义模型架构-
model = Sequential() model.add(Conv2D(32, 3, 3, activation = 'relu', input_shape = (28,28,1))) model.add(Conv2D(32, 3, 3, activation = 'relu')) model.add(MaxPool2D(pool_size = (2,2))) model.add(Dropout(0.25)) model.add(Flatten()) model.add(Dense(128, activation = 'relu')) model.add(Dropout(0.5)) model.add(Dense(10, activation = 'softmax'))
步骤3 - 现在让编译指定的模型-
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
步骤4 - 现在，将使用训练数据拟合模型-
model.fit(X_train, Y_train, batch_size=32, epochs=10, verbose=1)
创建的迭代的输出如下-
Epoch 1/10 60000/60000 [==============================] - 65s - loss: 0.2124 - acc: 0.9345 Epoch 2/10 60000/60000 [==============================] - 62s - loss: 0.0893 - acc: 0.9740 Epoch 3/10 60000/60000 [==============================] - 58s - loss: 0.0665 - acc: 0.9802 Epoch 4/10 60000/60000 [==============================] - 62s - loss: 0.0571 - acc: 0.9830 Epoch 5/10 60000/60000 [==============================] - 62s - loss: 0.0474 - acc: 0.9855 Epoch 6/10 60000/60000 [==============================] - 59s - loss: 0.0416 - acc: 0.9871 Epoch 7/10 60000/60000 [==============================] - 61s - loss: 0.0380 - acc: 0.9877 Epoch 8/10 60000/60000 [==============================] - 63s - loss: 0.0333 - acc: 0.9895 Epoch 9/10 60000/60000 [==============================] - 64s - loss: 0.0325 - acc: 0.9898 Epoch 10/10 60000/60000 [==============================] - 60s - loss: 0.0284 - acc: 0.9910
参考链接
https://www.learnfk.com/tensorflow/tensorflow-keras.html
标签：10,60000,Keras,无涯,Epoch,train,loss,TensorFlow,model From： https://blog.51cto.com/u_14033984/7160431