宣告
本文參考Deep-Learning-Specialization-Coursera/Convolution_model_Application.ipynb at main · abdur75648/Deep-Learning-Specialization-Coursera · GitHub，力求理解，
資料下載
鏈接：https://pan.baidu.com/s/1xANUtgIWgt7gcul6dnInhA
提取碼：oajj，請在開始之前下載好所需資料
1 - Packages
首先，我們要匯入一些庫
import math import numpy as np import h5py import matplotlib.pyplot as plt from matplotlib.pyplot import imread import scipy from PIL import Image import pandas as pd import tensorflow as tf import tensorflow.keras.layers as tfl from tensorflow.python.framework import ops from cnn_utils import * from test_utils import summary, comparator %matplotlib inline np.random.seed(1)
資料集我們使用的是 Happy House dataset
加載一下資料集吧！
X_train_orig, Y_train_orig, X_test_orig, Y_test_orig, classes = load_happy_dataset() # Normalize image vectors X_train = X_train_orig/255. X_test = X_test_orig/255. # Reshape Y_train = Y_train_orig.T Y_test = Y_test_orig.T print ("number of training examples = " + str(X_train.shape[0])) print ("number of test examples = " + str(X_test.shape[0])) print ("X_train shape: " + str(X_train.shape)) print ("Y_train shape: " + str(Y_train.shape)) print ("X_test shape: " + str(X_test.shape)) print ("Y_test shape: " + str(Y_test.shape))
number of training examples = 600 number of test examples = 150 X_train shape: (600, 64, 64, 3) Y_train shape: (600, 1) X_test shape: (150, 64, 64, 3) Y_test shape: (150, 1)
結果發現我們使用的是64?64，3通道的圖片
我們隨機的看張圖片
index = 120 plt.imshow(X_train_orig[index]) #display sample training image plt.show()
happyModel
- ZeroPadding2D: 填充三層 ，輸入應該為64 x 64 x 3
- Conv2D: 使用 32 個7x7 filters, stride 1
- BatchNormalization: for axis 3
- ReLU
- MaxPool2D: Using default parameters
- Flatten the previous output.
Fully-connected (Dense) layer: Apply a fully connected layer with 1 neuron and a sigmoid activation.
def happyModel(): """ 實作二進制分類模型的正向傳播： ZEROPAD2D -> CONV2D -> BATCHNORM -> RELU -> MAXPOOL -> FLATTEN -> DENSE 注意，為了簡化和分級，您將對所有值進行硬編碼 例如步幅和內核（濾波器）大小， 通常，函式應將這些值作為函式引數， Arguments: None Returns: model -- TF Keras model (包含整個培訓程序資訊的物件) """ model = tf.keras.Sequential([ tf.keras.Input(shape=(64 , 64 ,3)), ## ZeroPadding2D with padding 3, input shape of 64 x 64 x 3 tfl.ZeroPadding2D(padding=3), # As import tensorflow.keras.layers as tfl ## Conv2D with 32 7x7 filters and stride of 1 tfl.Conv2D(filters=32,kernel_size=7,strides=1), ## BatchNormalization for axis 3 tfl.BatchNormalization(axis=3, momentum=0.99, epsilon=0.001), ## ReLU tfl.ReLU(), ## Max Pooling 2D with default parameters tfl.MaxPool2D(), ## Flatten layer tfl.Flatten(), ## Dense layer with 1 unit for output & 'sigmoid' activation tfl.Dense(1,activation='sigmoid') ]) return model
我們來測驗一下：
happy_model = happyModel() # Print a summary for each layer for layer in summary(happy_model): print(layer)
['ZeroPadding2D', (None, 70, 70, 3), 0, ((3, 3), (3, 3))] ['Conv2D', (None, 64, 64, 32), 4736, 'valid', 'linear', 'GlorotUniform'] ['BatchNormalization', (None, 64, 64, 32), 128] ['ReLU', (None, 64, 64, 32), 0] ['MaxPooling2D', (None, 32, 32, 32), 0, (2, 2), (2, 2), 'valid'] ['Flatten', (None, 32768), 0] ['Dense', (None, 1), 32769, 'sigmoid']
現在您的模型已經創建，您可以編譯它，以便使用優化器進行訓練，而不必選擇，當字串精度指定為度量時，所使用的精度型別將根據所使用的損失函式自動轉換，這是TensorFlow內置的眾多優化之一，可以讓您的生活更輕松！
好了，讓我們去編譯它吧！
happy_model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
是時候用.summary（）方法檢查模型的引數了，這將顯示您擁有的圖層型別、輸出的形狀以及每個圖層中的引數數量，
happy_model.summary()
Model: "sequential"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
zero_padding2d (ZeroPadding (None, 70, 70, 3) 0
2D)
conv2d (Conv2D) (None, 64, 64, 32) 4736
batch_normalization (BatchN (None, 64, 64, 32) 128
ormalization)
re_lu (ReLU) (None, 64, 64, 32) 0
max_pooling2d (MaxPooling2D (None, 32, 32, 32) 0
)
flatten (Flatten) (None, 32768) 0
dense (Dense) (None, 1) 32769
=================================================================
Total params: 37,633
Trainable params: 37,569
Non-trainable params: 64
_________________________________________________________________
Train and Evaluate the Model
在創建了模型，使用您選擇的優化器和損失函式對其進行編譯，并對其內容進行了完整性檢查之后，現在就可以開始構建了！
只需呼叫.fit（）進行訓練，就是這樣！無需進行小批量、節省或復雜的反向傳播計算，這一切都是為您完成的，因為您使用的是已經指定批次的TensorFlow資料集，如果您愿意，您可以選擇指定紀元編號或小批量大小（例如，在未批處理資料集的情況下），
happy_model.evaluate(X_test, Y_test)
5/5 [==============================] - 0s 10ms/step - loss: 0.1438 - accuracy: 0.9400Out[28]:
[0.14378029108047485, 0.9399999976158142]
很簡單，對吧？但是，如果您需要構建一個具有共享層、分支或多個輸入和輸出的模型呢？這正是Sequential憑借其簡單而有限的功能無法幫助您的地方，
下一步：進入Functional API，這是一個稍微復雜、高度靈活的朋友，
歡迎來到作業的下半部分，您將使用Keras靈活的Functional API構建一個ConvNet，它可以區分6個手語數字，
Functional API可以處理具有非線性拓撲、共享層以及具有多個輸入或輸出的層的模型，想象一下，當Sequential API要求模型以線性方式在其層中移動時，Functional API允許更大的靈活性，在Sequential是一條直線的情況下，Functional模型是一個圖，其中層的節點可以以多種方式連接，
在下面的視覺示例中，順序模型顯示了一個可能的運動方向，與跳過連接形成對比，這只是構建功能模型的多種方式之一，您可能已經猜到，跳過連接會跳過網路中的某一層，并將輸出饋送到網路中的稍后一層，別擔心，你很快就會花更多時間在跳過連接上！
我們加載一下資料集
X_train_orig, Y_train_orig, X_test_orig, Y_test_orig, classes = load_signs_dataset()
隨機看一個資料
index = 4 plt.imshow(X_train_orig[index]) print ("y = " + str(np.squeeze(Y_train_orig[:, index])))
y = 2
在之前的學習中，
您為該資料集構建了一個完全連接的網路，但由于這是一個影像資料集，因此將ConvNet應用于它更為自然，
首先，讓我們檢查資料的形狀，
X_train = X_train_orig/255. X_test = X_test_orig/255. Y_train = convert_to_one_hot(Y_train_orig, 6).T Y_test = convert_to_one_hot(Y_test_orig, 6).T print ("number of training examples = " + str(X_train.shape[0])) print ("number of test examples = " + str(X_test.shape[0])) print ("X_train shape: " + str(X_train.shape)) print ("Y_train shape: " + str(Y_train.shape)) print ("X_test shape: " + str(X_test.shape)) print ("Y_test shape: " + str(Y_test.shape))
number of training examples = 1080 number of test examples = 120 X_train shape: (1080, 64, 64, 3) Y_train shape: (1080, 6) X_test shape: (120, 64, 64, 3) Y_test shape: (120, 6)
Forward Propagation
- Conv2D:8個4?4的 filters, stride 1, 填充 is "SAME"
- ReLU
- MaxPool2D: 使用一個8?8 filter size and 一個（8，8）步長, padding is "SAME"
- Conv2D: Use 16 2 by 2 filters, stride 1, padding is "SAME"
- ReLU
- MaxPool2D: Use a 4 by 4 filter size and a 4 by 4 stride, padding is "SAME"
- Flatten the previous output.
- Fully-connected (Dense) layer:應用具有6個神經元的完全連接層和softmax激活，
下面我們建立模型
def convolutional_model(input_shape): """ Implements the forward propagation for the model: CONV2D -> RELU -> MAXPOOL -> CONV2D -> RELU -> MAXPOOL -> FLATTEN -> DENSE 注意，為了簡化和分級，您將硬編碼一些值 例如步幅和內核（濾波器）大小， 通常，函式應將這些值作為函式引數， Arguments: input_img -- input dataset, of shape (input_shape) Returns: model -- TF Keras model (包含整個培訓程序資訊的物件) """ input_img = tf.keras.Input(shape=input_shape) ## CONV2D: 8 filters 4x4, stride of 1, padding 'SAME' Z1 = tfl.Conv2D(filters= 8. , kernel_size=4 , padding='same',strides=1)(input_img) ## RELU A1 = tfl.ReLU()(Z1) ## MAXPOOL: window 8x8, stride 8, padding 'SAME' P1 = tfl.MaxPool2D(pool_size=8, strides=8, padding='SAME')(A1) ## CONV2D: 16 filters 2x2, stride 1, padding 'SAME' Z2 = tfl.Conv2D(filters= 16. , kernel_size=2 , padding='same',strides=1)(P1) ## RELU A2 = tfl.ReLU()(Z2) ## MAXPOOL: window 4x4, stride 4, padding 'SAME' P2 = tfl.MaxPool2D(pool_size=4, strides=4, padding='SAME')(A2) ## FLATTEN F = tfl.Flatten()(P2) ## 全連接層 ##輸出層6個神經元，提示：其中一個引數應該是“activation='softmax'” outputs = tfl.Dense(units= 6 , activation='softmax')(F) model = tf.keras.Model(inputs=input_img, outputs=outputs) return model
編譯并檢查模型的引數
conv_model = convolutional_model((64, 64, 3)) conv_model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy']) conv_model.summary()
Model: "model"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
input_2 (InputLayer) [(None, 64, 64, 3)] 0
conv2d_1 (Conv2D) (None, 64, 64, 8) 392
re_lu_1 (ReLU) (None, 64, 64, 8) 0
max_pooling2d_1 (MaxPooling (None, 8, 8, 8) 0
2D)
conv2d_2 (Conv2D) (None, 8, 8, 16) 528
re_lu_2 (ReLU) (None, 8, 8, 16) 0
max_pooling2d_2 (MaxPooling (None, 2, 2, 16) 0
2D)
flatten_1 (Flatten) (None, 64) 0
dense_1 (Dense) (None, 6) 390
=================================================================
Total params: 1,310
Trainable params: 1,310
Non-trainable params: 0
_________________________________________________________________
Train the Model
train_dataset = tf.data.Dataset.from_tensor_slices((X_train, Y_train)).batch(64) test_dataset = tf.data.Dataset.from_tensor_slices((X_test, Y_test)).batch(64) history = conv_model.fit(train_dataset, epochs=100, validation_data=https://www.cnblogs.com/kk-style/archive/2022/11/19/test_dataset)
Epoch 1/100 17/17 [==============================] - 1s 52ms/step - loss: 1.7924 - accuracy: 0.1870 - val_loss: 1.7881 - val_accuracy: 0.1917 Epoch 2/100 17/17 [==============================] - 1s 64ms/step - loss: 1.7830 - accuracy: 0.2389 - val_loss: 1.7836 - val_accuracy: 0.2250 Epoch 3/100 17/17 [==============================] - 1s 69ms/step - loss: 1.7775 - accuracy: 0.2574 - val_loss: 1.7797 - val_accuracy: 0.1917 Epoch 4/100 17/17 [==============================] - 1s 60ms/step - loss: 1.7715 - accuracy: 0.2620 - val_loss: 1.7742 - val_accuracy: 0.2333 Epoch 5/100 17/17 [==============================] - 1s 54ms/step - loss: 1.7632 - accuracy: 0.3102 - val_loss: 1.7679 - val_accuracy: 0.2917 Epoch 6/100 17/17 [==============================] - 1s 59ms/step - loss: 1.7526 - accuracy: 0.3519 - val_loss: 1.7582 - val_accuracy: 0.3500 Epoch 7/100 17/17 [==============================] - 1s 63ms/step - loss: 1.7387 - accuracy: 0.3731 - val_loss: 1.7453 - val_accuracy: 0.3417 Epoch 8/100 17/17 [==============================] - 1s 62ms/step - loss: 1.7181 - accuracy: 0.3935 - val_loss: 1.7270 - val_accuracy: 0.3333 Epoch 9/100 17/17 [==============================] - 1s 55ms/step - loss: 1.6928 - accuracy: 0.4250 - val_loss: 1.7027 - val_accuracy: 0.3667 Epoch 10/100 17/17 [==============================] - 1s 51ms/step - loss: 1.6624 - accuracy: 0.4472 - val_loss: 1.6717 - val_accuracy: 0.3750 Epoch 11/100 17/17 [==============================] - 1s 44ms/step - loss: 1.6234 - accuracy: 0.4722 - val_loss: 1.6347 - val_accuracy: 0.4167 Epoch 12/100 17/17 [==============================] - 1s 41ms/step - loss: 1.5788 - accuracy: 0.4833 - val_loss: 1.5910 - val_accuracy: 0.4667 Epoch 13/100 17/17 [==============================] - 1s 49ms/step - loss: 1.5306 - accuracy: 0.5028 - val_loss: 1.5451 - val_accuracy: 0.5083 Epoch 14/100 17/17 [==============================] - 1s 50ms/step - loss: 1.4796 - accuracy: 0.5194 - val_loss: 1.4939 - val_accuracy: 0.5000 Epoch 15/100 17/17 [==============================] - 1s 48ms/step - loss: 1.4250 - accuracy: 0.5370 - val_loss: 1.4377 - val_accuracy: 0.5417 Epoch 16/100 17/17 [==============================] - 1s 43ms/step - loss: 1.3661 - accuracy: 0.5574 - val_loss: 1.3788 - val_accuracy: 0.5750 Epoch 17/100 17/17 [==============================] - 1s 49ms/step - loss: 1.3062 - accuracy: 0.5694 - val_loss: 1.3132 - val_accuracy: 0.6083 Epoch 18/100 17/17 [==============================] - 1s 43ms/step - loss: 1.2476 - accuracy: 0.5981 - val_loss: 1.2558 - val_accuracy: 0.5833 Epoch 19/100 17/17 [==============================] - 1s 44ms/step - loss: 1.1896 - accuracy: 0.6278 - val_loss: 1.2034 - val_accuracy: 0.6167 Epoch 20/100 17/17 [==============================] - 1s 45ms/step - loss: 1.1389 - accuracy: 0.6426 - val_loss: 1.1515 - val_accuracy: 0.6417 Epoch 21/100 17/17 [==============================] - 1s 44ms/step - loss: 1.0976 - accuracy: 0.6519 - val_loss: 1.1115 - val_accuracy: 0.6417 Epoch 22/100 17/17 [==============================] - 1s 49ms/step - loss: 1.0567 - accuracy: 0.6565 - val_loss: 1.0731 - val_accuracy: 0.6250 Epoch 23/100 17/17 [==============================] - 1s 48ms/step - loss: 1.0229 - accuracy: 0.6685 - val_loss: 1.0447 - val_accuracy: 0.6333 Epoch 24/100 17/17 [==============================] - 1s 49ms/step - loss: 0.9881 - accuracy: 0.6722 - val_loss: 1.0128 - val_accuracy: 0.6417 Epoch 25/100 17/17 [==============================] - 1s 53ms/step - loss: 0.9586 - accuracy: 0.6880 - val_loss: 0.9859 - val_accuracy: 0.6500 Epoch 26/100 17/17 [==============================] - 1s 42ms/step - loss: 0.9345 - accuracy: 0.6954 - val_loss: 0.9655 - val_accuracy: 0.6500 Epoch 27/100 17/17 [==============================] - 1s 56ms/step - loss: 0.9080 - accuracy: 0.7009 - val_loss: 0.9405 - val_accuracy: 0.6583 Epoch 28/100 17/17 [==============================] - 1s 44ms/step - loss: 0.8859 - accuracy: 0.7120 - val_loss: 0.9210 - val_accuracy: 0.6667 Epoch 29/100 17/17 [==============================] - 1s 46ms/step - loss: 0.8638 - accuracy: 0.7213 - val_loss: 0.8993 - val_accuracy: 0.6667 Epoch 30/100 17/17 [==============================] - 1s 44ms/step - loss: 0.8460 - accuracy: 0.7324 - val_loss: 0.8815 - val_accuracy: 0.6667 Epoch 31/100 17/17 [==============================] - 1s 45ms/step - loss: 0.8278 - accuracy: 0.7389 - val_loss: 0.8654 - val_accuracy: 0.6667 Epoch 32/100 17/17 [==============================] - 1s 65ms/step - loss: 0.8084 - accuracy: 0.7426 - val_loss: 0.8504 - val_accuracy: 0.6750 Epoch 33/100 17/17 [==============================] - 1s 47ms/step - loss: 0.7896 - accuracy: 0.7509 - val_loss: 0.8345 - val_accuracy: 0.6833 Epoch 34/100 17/17 [==============================] - 1s 63ms/step - loss: 0.7741 - accuracy: 0.7537 - val_loss: 0.8211 - val_accuracy: 0.7000 Epoch 35/100 17/17 [==============================] - 1s 48ms/step - loss: 0.7585 - accuracy: 0.7565 - val_loss: 0.8074 - val_accuracy: 0.7083 Epoch 36/100 17/17 [==============================] - 1s 48ms/step - loss: 0.7439 - accuracy: 0.7639 - val_loss: 0.7955 - val_accuracy: 0.7083 Epoch 37/100 17/17 [==============================] - 1s 52ms/step - loss: 0.7297 - accuracy: 0.7694 - val_loss: 0.7830 - val_accuracy: 0.7083 Epoch 38/100 17/17 [==============================] - 1s 48ms/step - loss: 0.7170 - accuracy: 0.7741 - val_loss: 0.7712 - val_accuracy: 0.7250 Epoch 39/100 17/17 [==============================] - 1s 47ms/step - loss: 0.7036 - accuracy: 0.7731 - val_loss: 0.7596 - val_accuracy: 0.7333 Epoch 40/100 17/17 [==============================] - 1s 44ms/step - loss: 0.6921 - accuracy: 0.7824 - val_loss: 0.7491 - val_accuracy: 0.7417 Epoch 41/100 17/17 [==============================] - 1s 64ms/step - loss: 0.6797 - accuracy: 0.7843 - val_loss: 0.7382 - val_accuracy: 0.7583 Epoch 42/100 17/17 [==============================] - 1s 48ms/step - loss: 0.6682 - accuracy: 0.7917 - val_loss: 0.7290 - val_accuracy: 0.7667 Epoch 43/100 17/17 [==============================] - 1s 66ms/step - loss: 0.6567 - accuracy: 0.7963 - val_loss: 0.7184 - val_accuracy: 0.7750 Epoch 44/100 17/17 [==============================] - 1s 42ms/step - loss: 0.6469 - accuracy: 0.7991 - val_loss: 0.7106 - val_accuracy: 0.7750 Epoch 45/100 17/17 [==============================] - 1s 45ms/step - loss: 0.6362 - accuracy: 0.8009 - val_loss: 0.7016 - val_accuracy: 0.7750 Epoch 46/100 17/17 [==============================] - 1s 57ms/step - loss: 0.6263 - accuracy: 0.8019 - val_loss: 0.6939 - val_accuracy: 0.7750 Epoch 47/100 17/17 [==============================] - 1s 45ms/step - loss: 0.6172 - accuracy: 0.8065 - val_loss: 0.6859 - val_accuracy: 0.7833 Epoch 48/100 17/17 [==============================] - 1s 49ms/step - loss: 0.6076 - accuracy: 0.8083 - val_loss: 0.6784 - val_accuracy: 0.7917 Epoch 49/100 17/17 [==============================] - 1s 47ms/step - loss: 0.5992 - accuracy: 0.8102 - val_loss: 0.6711 - val_accuracy: 0.7917 Epoch 50/100 17/17 [==============================] - 1s 62ms/step - loss: 0.5904 - accuracy: 0.8093 - val_loss: 0.6638 - val_accuracy: 0.8000 Epoch 51/100 17/17 [==============================] - 1s 48ms/step - loss: 0.5822 - accuracy: 0.8120 - val_loss: 0.6571 - val_accuracy: 0.8000 Epoch 52/100 17/17 [==============================] - 1s 56ms/step - loss: 0.5736 - accuracy: 0.8157 - val_loss: 0.6508 - val_accuracy: 0.8083 Epoch 53/100 17/17 [==============================] - 1s 53ms/step - loss: 0.5664 - accuracy: 0.8204 - val_loss: 0.6448 - val_accuracy: 0.8083 Epoch 54/100 17/17 [==============================] - 1s 56ms/step - loss: 0.5586 - accuracy: 0.8194 - val_loss: 0.6385 - val_accuracy: 0.8083 Epoch 55/100 17/17 [==============================] - 1s 58ms/step - loss: 0.5509 - accuracy: 0.8204 - val_loss: 0.6341 - val_accuracy: 0.8083 Epoch 56/100 17/17 [==============================] - 1s 51ms/step - loss: 0.5444 - accuracy: 0.8222 - val_loss: 0.6277 - val_accuracy: 0.8000 Epoch 57/100 17/17 [==============================] - 1s 62ms/step - loss: 0.5386 - accuracy: 0.8222 - val_loss: 0.6241 - val_accuracy: 0.8000 Epoch 58/100
17/17 [==============================] - 1s 48ms/step - loss: 0.5296 - accuracy: 0.8296 - val_loss: 0.6193 - val_accuracy: 0.8167
Epoch 59/100 17/17 [==============================] - 1s 63ms/step - loss: 0.5240 - accuracy: 0.8296 - val_loss: 0.6141 - val_accuracy: 0.8000 Epoch 60/100 17/17 [==============================] - 1s 45ms/step - loss: 0.5175 - accuracy: 0.8333 - val_loss: 0.6084 - val_accuracy: 0.8083 Epoch 61/100 17/17 [==============================] - 1s 53ms/step - loss: 0.5093 - accuracy: 0.8352 - val_loss: 0.6047 - val_accuracy: 0.8250 Epoch 62/100 17/17 [==============================] - 1s 50ms/step - loss: 0.5044 - accuracy: 0.8370 - val_loss: 0.5991 - val_accuracy: 0.8083 Epoch 63/100 17/17 [==============================] - 1s 53ms/step - loss: 0.4981 - accuracy: 0.8333 - val_loss: 0.5955 - val_accuracy: 0.8167 Epoch 64/100 17/17 [==============================] - 1s 57ms/step - loss: 0.4902 - accuracy: 0.8380 - val_loss: 0.5926 - val_accuracy: 0.8250 Epoch 65/100 17/17 [==============================] - 1s 48ms/step - loss: 0.4853 - accuracy: 0.8444 - val_loss: 0.5882 - val_accuracy: 0.8083 Epoch 66/100 17/17 [==============================] - 1s 48ms/step - loss: 0.4794 - accuracy: 0.8472 - val_loss: 0.5839 - val_accuracy: 0.8083 Epoch 67/100 17/17 [==============================] - 1s 44ms/step - loss: 0.4724 - accuracy: 0.8519 - val_loss: 0.5809 - val_accuracy: 0.8167 Epoch 68/100 17/17 [==============================] - 1s 54ms/step - loss: 0.4680 - accuracy: 0.8528 - val_loss: 0.5760 - val_accuracy: 0.8083 Epoch 69/100 17/17 [==============================] - 1s 46ms/step - loss: 0.4623 - accuracy: 0.8546 - val_loss: 0.5719 - val_accuracy: 0.8250 Epoch 70/100 17/17 [==============================] - 1s 49ms/step - loss: 0.4559 - accuracy: 0.8574 - val_loss: 0.5692 - val_accuracy: 0.8083 Epoch 71/100 17/17 [==============================] - 1s 57ms/step - loss: 0.4518 - accuracy: 0.8593 - val_loss: 0.5650 - val_accuracy: 0.8083 Epoch 72/100 17/17 [==============================] - 1s 60ms/step - loss: 0.4452 - accuracy: 0.8620 - val_loss: 0.5624 - val_accuracy: 0.8250 Epoch 73/100 17/17 [==============================] - 1s 52ms/step - loss: 0.4415 - accuracy: 0.8639 - val_loss: 0.5590 - val_accuracy: 0.8167 Epoch 74/100 17/17 [==============================] - 1s 55ms/step - loss: 0.4361 - accuracy: 0.8648 - val_loss: 0.5554 - val_accuracy: 0.8250 Epoch 75/100 17/17 [==============================] - 1s 58ms/step - loss: 0.4298 - accuracy: 0.8704 - val_loss: 0.5528 - val_accuracy: 0.8333 Epoch 76/100 17/17 [==============================] - 1s 62ms/step - loss: 0.4262 - accuracy: 0.8685 - val_loss: 0.5490 - val_accuracy: 0.8250 Epoch 77/100 17/17 [==============================] - 1s 48ms/step - loss: 0.4215 - accuracy: 0.8713 - val_loss: 0.5460 - val_accuracy: 0.8250 Epoch 78/100 17/17 [==============================] - 1s 46ms/step - loss: 0.4151 - accuracy: 0.8787 - val_loss: 0.5436 - val_accuracy: 0.8250 Epoch 79/100 17/17 [==============================] - 1s 43ms/step - loss: 0.4113 - accuracy: 0.8787 - val_loss: 0.5407 - val_accuracy: 0.8167 Epoch 80/100 17/17 [==============================] - 1s 43ms/step - loss: 0.4062 - accuracy: 0.8806 - val_loss: 0.5384 - val_accuracy: 0.8167 Epoch 81/100 17/17 [==============================] - 1s 48ms/step - loss: 0.4020 - accuracy: 0.8806 - val_loss: 0.5348 - val_accuracy: 0.8167 Epoch 82/100 17/17 [==============================] - 1s 50ms/step - loss: 0.3962 - accuracy: 0.8824 - val_loss: 0.5323 - val_accuracy: 0.8167 Epoch 83/100 17/17 [==============================] - 1s 45ms/step - loss: 0.3927 - accuracy: 0.8824 - val_loss: 0.5297 - val_accuracy: 0.8250 Epoch 84/100 17/17 [==============================] - 1s 51ms/step - loss: 0.3881 - accuracy: 0.8843 - val_loss: 0.5272 - val_accuracy: 0.8250 Epoch 85/100 17/17 [==============================] - 1s 46ms/step - loss: 0.3832 - accuracy: 0.8870 - val_loss: 0.5249 - val_accuracy: 0.8250 Epoch 86/100 17/17 [==============================] - 1s 53ms/step - loss: 0.3796 - accuracy: 0.8898 - val_loss: 0.5215 - val_accuracy: 0.8250 Epoch 87/100 17/17 [==============================] - 1s 56ms/step - loss: 0.3743 - accuracy: 0.8889 - val_loss: 0.5196 - val_accuracy: 0.8250 Epoch 88/100 17/17 [==============================] - 1s 48ms/step - loss: 0.3710 - accuracy: 0.8907 - val_loss: 0.5164 - val_accuracy: 0.8250 Epoch 89/100 17/17 [==============================] - 1s 45ms/step - loss: 0.3660 - accuracy: 0.8917 - val_loss: 0.5139 - val_accuracy: 0.8333 Epoch 90/100 17/17 [==============================] - 1s 45ms/step - loss: 0.3626 - accuracy: 0.8917 - val_loss: 0.5106 - val_accuracy: 0.8333 Epoch 91/100 17/17 [==============================] - 1s 48ms/step - loss: 0.3579 - accuracy: 0.8944 - val_loss: 0.5090 - val_accuracy: 0.8500 Epoch 92/100 17/17 [==============================] - 1s 49ms/step - loss: 0.3547 - accuracy: 0.8935 - val_loss: 0.5060 - val_accuracy: 0.8417 Epoch 93/100 17/17 [==============================] - 1s 44ms/step - loss: 0.3501 - accuracy: 0.8944 - val_loss: 0.5038 - val_accuracy: 0.8500 Epoch 94/100 17/17 [==============================] - 1s 47ms/step - loss: 0.3468 - accuracy: 0.8954 - val_loss: 0.5014 - val_accuracy: 0.8417 Epoch 95/100 17/17 [==============================] - 1s 43ms/step - loss: 0.3424 - accuracy: 0.8954 - val_loss: 0.4996 - val_accuracy: 0.8500 Epoch 96/100 17/17 [==============================] - 1s 64ms/step - loss: 0.3395 - accuracy: 0.8963 - val_loss: 0.4970 - val_accuracy: 0.8417 Epoch 97/100 17/17 [==============================] - 1s 47ms/step - loss: 0.3351 - accuracy: 0.9000 - val_loss: 0.4950 - val_accuracy: 0.8417 Epoch 98/100 17/17 [==============================] - 1s 54ms/step - loss: 0.3323 - accuracy: 0.8981 - val_loss: 0.4933 - val_accuracy: 0.8333 Epoch 99/100 17/17 [==============================] - 1s 48ms/step - loss: 0.3280 - accuracy: 0.9000 - val_loss: 0.4916 - val_accuracy: 0.8417 Epoch 100/100 17/17 [==============================] - 1s 57ms/step - loss: 0.3251 - accuracy: 0.9028 - val_loss: 0.4894 - val_accuracy: 0.8333
history物件是.fit（）操作的輸出，并提供記憶體中所有損失和度量值的記錄，它存盤為字典，您可以在history中檢索，history：
history.history
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現在，使用history.history可視化時間損失：
df_loss_acc = pd.DataFrame(history.history) df_loss= df_loss_acc[['loss','val_loss']] df_loss.rename(columns={'loss':'train','val_loss':'validation'},inplace=True) df_acc= df_loss_acc[['accuracy','val_accuracy']] df_acc.rename(columns={'accuracy':'train','val_accuracy':'validation'},inplace=True) df_loss.plot(title='Model loss',figsize=(12,8)).set(xlabel='Epoch',ylabel='Loss') df_acc.plot(title='Model Accuracy',figsize=(12,8)).set(xlabel='Epoch',ylabel='Accuracy') plt.show()
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