📅 最后修改于: 2020-12-10 06:04:43 🧑 作者: Mango
Keras是紧凑，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，例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：
考虑以下八个步骤以在Keras中创建深度学习模型-
我们将使用Jupyter Notebook执行和显示输出，如下所示-
步骤1-首先执行加载数据并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)
可以将该步骤定义为“导入库和模块”，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，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'))
步骤3-现在让我们编译指定的模型-
model.compile(loss = 'categorical_crossentropy', optimizer = 'adam', metrics = ['accuracy'])
步骤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