简述
Keras 是运行在 TensorFlow 框架之上的紧凑、易于学习的高级 Python 库。它专注于理解深度学习技术，例如为保持形状和数学细节概念的神经网络创建层。创作框架作品可以是以下两种类型 -
考虑以下八个步骤在 Keras 中创建深度学习模型 -
- 加载数据
- 对加载的数据进行预处理
- 模型定义
- 编译模型
- 拟合指定模型
- 评估一下
- 进行所需的预测
- 保存模型
我们将使用 Jupyter Notebook 执行和显示输出，如下所示 -
Step 1 − 加载数据并对加载的数据进行预处理，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)
这一步可以定义为“导入库和模块”，这意味着所有库和模块都是作为初始步骤导入的。
Step 2 − 在这一步中，我们将定义模型架构 −
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'))
Step 3 - 现在让我们编译指定的模型 -
model.compile(loss = 'categorical_crossentropy', optimizer = 'adam', metrics = ['accuracy'])
Step 4 − 我们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
