Keras是一款紧凑，易于学习的高级Python库，运行在TensorFlow框架之上.它的重点是理解深度学习技术，例如为神经网络创建维护形状和数学细节概念的层. freamework的创建可以是以下两种类型 :
顺序API
功能API
考虑以下八个步骤在Keras中创建深度学习模型 :
加载数据
预处理加载的数据
模型定义
编译模型
适合指定的模型
评估它
进行必要的预测
保存模型
我们将使用Jupyter Notebook执行和显示输出，如下所示 :
第1步 : 首先实现加载数据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
# Load pre-shuffled MNIST data into train and test sets
(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)此步骤可以定义为"导入库和模块"表示所有库和模块都作为初始步骤导入.
步骤2 : 在这一步中，我们将定义模型体系结构 :
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'))
第3步 : 现在让我们编译指定的模型 :
model.compile(loss = 'categorical_crossentropy', optimizer = 'adam', metrics = ['accuracy'])
第4步 : 我们现在将使用训练数据和减去拟合模型;
model.fit(X_train, Y_train, batch_size = 32, epochs = 10, verbose = 1)
创建的迭代输出如下 :
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
