Keras是緊湊，易於學習的高階Python庫，執行在TensorFlow框架之上。它的重點是理解深度學習技術，例如為神經網路建立維護形狀和數學細節概念的層。freamework的建立可以是以下兩種型別 -
在Keras中建立深度學習模型有以下 8 個步驟 -
下面將使用Jupyter Notebook執行和顯示輸出，如下所示 -
第1步 - 首先實現載入資料和預處理載入的資料以執行深度學習模型。
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)
此步驟可以定義為「匯入庫和模組」，將所有庫和模組作為初始步驟匯入。
第2步 - 在這一步中定義模型架構 -
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 - 現在編譯指定的模型 -
model.compile(loss = 'categorical_crossentropy', optimizer = 'adam', metrics = ['accuracy'])
第4步 - 現在將使用訓練資料擬合模型 -
model.fit(X_train, Y_train, batch_size = 32, epochs = 10, verbose = 1)
建立的疊代輸出如下 -
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