構(gòu)建一個字母ABC的手寫識別網(wǎng)絡,

要求給出算法誤差收斂曲線，所給程序要有圖片導入接口。

其中A，B，C都代表label，三個文件夾存在具體的圖片。只要是這樣類型的，直接套下面模板。

import?os import?cv2 import?numpy?as?np import?pandas?as?pd import?matplotlib.pyplot?as?plt import?tensorflow?as?tf import?tqdmfrom?tensorflow?import?keras from?keras?import?Input,?Model,?Sequential from?tensorflow.keras.regularizers?import?l2 from?keras.layers?import?Dense,?Flatten,?InputLayer,?Reshape,?BatchNormalization,?Dropout,?Conv2D,?MaxPooling2D from?tensorflow.keras.utils?import?plot_model%matplotlib?inlinedata_dir?=?'./data'#?Dict?of?labels categories?=?{ 'A':?0, 'B':?1, 'C':?2? }def?load_images(images_folder,?img_size?=?(128,128),?scale=False):#?Store?paths?to?imagesimage_path?=?[]for?dirname,?_,?filenames?in?os.walk(images_folder):for?filename?in?filenames:image_path.append(os.path.join(dirname,?filename))print("There?are?{}?images?in?{}".format(len(image_path),?images_folder))#?Load?images?and?associated?labelsimages?=?[]labels?=?[]for?path?in?tqdm.tqdm(image_path):img?=?cv2.imread(path)????img?=?cv2.resize(img,?img_size)?#?Resize?the?imagesimg?=?np.array(img)images.append(img)labels.append(categories[path.split('/')[-2]])?#?last?folder?before?the?image?name?is?the?categoryimages?=?np.array(images)??images?=?images.astype(np.int64)if?scale:images?=?images/255?#?scalereturn?image_path,?images,?np.asarray(labels)img_size?=?(128,128) image_path,?images,?labels?=?load_images(data_dir,?img_size=img_size)#?Resize #?images?=?np.array(images).reshape(-1,128,128,1) images.shapeThere are 600 images in ./data100%|██████████| 600/600 [00:03<00:00, 183.15it/s](600, 128, 128, 3)#?查看圖片 plt.figure(figsize=(10,10)) random_inds?=?np.random.choice(len(image_path),36) for?i?in?range(36):plt.subplot(6,6,i+1)plt.xticks([])plt.yticks([])plt.grid(False)image_ind?=?random_inds[i]plt.imshow(np.squeeze(images[image_ind]),?cmap=plt.cm.binary)label?=?list(categories.keys())[list(categories.values()).index(labels[image_ind])]plt.title(label)labels_df?=?pd.DataFrame(labels)? labels_df.value_counts()2 201 0 201 1 198 dtype: int64dataset=[] dataname=[] count=0 for?name?in?tqdm(os.listdir(data_dir)):path=os.path.join(data_dir,name)for?im?in?os.listdir(path):image=cv2.imread(os.path.join(path,im))image2=np.resize(image,(50,50,3))dataset+=[image2]dataname+=[count]count=count+1100%|██████████| 3/3 [00:03<00:00, 1.06s/it]data=np.array(dataset) dataname=np.array(dataname)data[0].shape(50, 50, 3)print(pd.Series(dataname).value_counts())1 202 2 201 0 198 dtype: int64len(categories)3from?tensorflow.keras.layers?import?Dense,?Conv2D,?Flatten,?Dropout,?MaxPooling2D,?BatchNormalizationdef?build_cnn_model():cnn_model=tf.keras.Sequential([Conv2D(filters=32,kernel_size=(3,3),activation='relu',input_shape=images.shape[1:]),MaxPooling2D(2,2),BatchNormalization(),Dropout(0.4),Conv2D(filters=64,kernel_size=(3,3),activation='relu',?padding='same'),Conv2D(filters=64,kernel_size=(3,3),activation='relu',?padding='same'),MaxPooling2D((2,2)),BatchNormalization(),Dropout(0.4),Conv2D(filters=128,kernel_size=(3,3),activation='relu',?padding='same'),Conv2D(filters=128,kernel_size=(3,3),activation='relu',?padding='same'),MaxPooling2D(2,2),BatchNormalization(),Dropout(0.4),Conv2D(filters=256,kernel_size=(3,3),activation='relu',?padding='same'),Conv2D(filters=256,kernel_size=(3,3),activation='relu',?padding='same'),MaxPooling2D(2,2),BatchNormalization(),Dropout(0.4),Conv2D(filters=128,kernel_size=(3,3),activation='relu',?padding='same'),Conv2D(filters=128,kernel_size=(3,3),activation='relu',?padding='same'),MaxPooling2D(2,2),BatchNormalization(),Dropout(0.4),Conv2D(filters=64,kernel_size=(3,3),activation='relu',?padding='same'),Conv2D(filters=64,kernel_size=(3,3),activation='relu',?padding='same'),MaxPooling2D((2,2)),BatchNormalization(),Dropout(0.4),Flatten(),Dense(units=len(categories),activation='softmax')])return?cnn_modelmodel?=?build_cnn_model() #?Initialize?the?model?by?passing?some?data?through model.predict(images[[0]]) #?Print?the?summary?of?the?layers?in?the?model. print(model.summary())Model: "sequential_3" _________________________________________________________________Layer (type) Output Shape Param # =================================================================conv2d_6 (Conv2D) (None, 126, 126, 32) 896 max_pooling2d_6 (MaxPooling (None, 63, 63, 32) 0 2D) batch_normalization (BatchN (None, 63, 63, 32) 128 ormalization) dropout (Dropout) (None, 63, 63, 32) 0 conv2d_7 (Conv2D) (None, 63, 63, 64) 18496 conv2d_8 (Conv2D) (None, 63, 63, 64) 36928 max_pooling2d_7 (MaxPooling (None, 31, 31, 64) 0 2D) batch_normalization_1 (Batc (None, 31, 31, 64) 256 hNormalization) dropout_1 (Dropout) (None, 31, 31, 64) 0 conv2d_9 (Conv2D) (None, 31, 31, 128) 73856 conv2d_10 (Conv2D) (None, 31, 31, 128) 147584 max_pooling2d_8 (MaxPooling (None, 15, 15, 128) 0 2D) batch_normalization_2 (Batc (None, 15, 15, 128) 512 hNormalization) dropout_2 (Dropout) (None, 15, 15, 128) 0 conv2d_11 (Conv2D) (None, 15, 15, 256) 295168 conv2d_12 (Conv2D) (None, 15, 15, 256) 590080 max_pooling2d_9 (MaxPooling (None, 7, 7, 256) 0 2D) batch_normalization_3 (Batc (None, 7, 7, 256) 1024 hNormalization) dropout_3 (Dropout) (None, 7, 7, 256) 0 conv2d_13 (Conv2D) (None, 7, 7, 128) 295040 conv2d_14 (Conv2D) (None, 7, 7, 128) 147584 max_pooling2d_10 (MaxPoolin (None, 3, 3, 128) 0 g2D) batch_normalization_4 (Batc (None, 3, 3, 128) 512 hNormalization) dropout_4 (Dropout) (None, 3, 3, 128) 0 conv2d_15 (Conv2D) (None, 3, 3, 64) 73792 conv2d_16 (Conv2D) (None, 3, 3, 64) 36928 max_pooling2d_11 (MaxPoolin (None, 1, 1, 64) 0 g2D) batch_normalization_5 (Batc (None, 1, 1, 64) 256 hNormalization) dropout_5 (Dropout) (None, 1, 1, 64) 0 flatten_1 (Flatten) (None, 64) 0 dense_6 (Dense) (None, 3) 195 ================================================================= Total params: 1,719,235 Trainable params: 1,717,891 Non-trainable params: 1,344 _________________________________________________________________ Nonetf.keras.utils.plot_model(model,?show_shapes=True)from?tensorflow.keras.utils?import?to_categorical from?sklearn.preprocessing?import?LabelEncoder from?sklearn.utils?import?shuffle le?=?LabelEncoder() labels?=?le.fit_transform(labels) labels?=?to_categorical(labels) labels[:10]array([[0., 1., 0.],[0., 1., 0.],[0., 1., 0.],[0., 1., 0.],[0., 1., 0.],[0., 1., 0.],[0., 1., 0.],[0., 1., 0.],[0., 1., 0.],[0., 1., 0.]], dtype=float32)model.compile(optimizer?=?"adam",?loss?=?"binary_crossentropy",?metrics?=?["accuracy"])checkpoint_filepath?=?'/checkpoint.hdf5' model_checkpoint_callback?=?tf.keras.callbacks.ModelCheckpoint(filepath=checkpoint_filepath,save_weights_only=True,monitor='val_accuracy',mode='max',save_best_only=True,save_freq=500)from?tensorflow.keras.callbacks?import?EarlyStopping early_stopping?=?EarlyStopping(monitor='val_accuracy',?patience=10,?min_delta=0.001,?mode='max',restore_best_weights=True )datagen?=?ImageDataGenerator(horizontal_flip=True,vertical_flip=True,rotation_range=20,zoom_range=0.2,width_shift_range=0.2,height_shift_range=0.2,shear_range=0.1,fill_mode="nearest")from?tensorflow.keras.callbacks?import?ReduceLROnPlateau reducelr?=?ReduceLROnPlateau(monitor?=?"val_accuracy",factor?=?0.3,?patience?=?3,min_delta?=?0.001,mode?=?'auto',verbose=1)from?sklearn.model_selection?import?train_test_split #?Train,?validation?and?test?splitX_train,?X_test,?y_train,?y_test?=?train_test_split(images,?labels,?test_size=0.10,?random_state=7)X_train,?X_val,?y_train,?y_val?=?train_test_split(X_train,?y_train,?test_size=0.20,?random_state=1)print("*-*-*-*-*-*") print("Train") print(X_train.shape) print(y_train.shape)print("*-*-*-*-*-*") print("Validation") print(X_val.shape) print(y_val.shape)print("*-*-*-*-*-*") print("Test") print(X_test.shape) print(y_test.shape)*-*-*-*-*-* Train (432, 128, 128, 3) (432, 3) *-*-*-*-*-* Validation (108, 128, 128, 3) (108, 3) *-*-*-*-*-* Test (60, 128, 128, 3) (60, 3)history?=?model.fit(X_train,?y_train,?batch_size?=?32,?epochs?=?100,?verbose?=?1,?validation_data?=?(X_val,?y_val),callbacks=[model_checkpoint_callback,?early_stopping,?reducelr])Epoch 1/1000 14/14 [==============================] - 8s 238ms/step - loss: 0.8036 - accuracy: 0.3588 - val_loss: 6.2921 - val_accuracy: 0.2963 - lr: 0.0010 Epoch 2/1000 14/14 [==============================] - 1s 95ms/step - loss: 0.8162 - accuracy: 0.3796 - val_loss: 5.2361 - val_accuracy: 0.2963 - lr: 0.0010 Epoch 3/1000 14/14 [==============================] - 1s 97ms/step - loss: 0.7190 - accuracy: 0.4537 - val_loss: 1.3893 - val_accuracy: 0.3333 - lr: 0.0010 Epoch 4/1000 14/14 [==============================] - 1s 97ms/step - loss: 0.6875 - accuracy: 0.4792 - val_loss: 0.7386 - val_accuracy: 0.3519 - lr: 0.0010 Epoch 5/1000 14/14 [==============================] - 1s 100ms/step - loss: 0.6144 - accuracy: 0.5949 - val_loss: 0.7014 - val_accuracy: 0.4259 - lr: 0.0010 Epoch 6/1000 14/14 [==============================] - 1s 97ms/step - loss: 0.5156 - accuracy: 0.7060 - val_loss: 0.7592 - val_accuracy: 0.4537 - lr: 0.0010 Epoch 7/1000 14/14 [==============================] - 1s 96ms/step - loss: 0.4904 - accuracy: 0.7384 - val_loss: 0.7034 - val_accuracy: 0.5370 - lr: 0.0010 Epoch 8/1000 14/14 [==============================] - 1s 97ms/step - loss: 0.3854 - accuracy: 0.7940 - val_loss: 0.6092 - val_accuracy: 0.5556 - lr: 0.0010 Epoch 9/1000 14/14 [==============================] - 1s 97ms/step - loss: 0.3313 - accuracy: 0.8241 - val_loss: 0.5192 - val_accuracy: 0.6389 - lr: 0.0010 Epoch 10/1000 14/14 [==============================] - 1s 93ms/step - loss: 0.2873 - accuracy: 0.8519 - val_loss: 0.5089 - val_accuracy: 0.6111 - lr: 0.0010 Epoch 11/1000 14/14 [==============================] - 1s 96ms/step - loss: 0.2346 - accuracy: 0.8981 - val_loss: 0.4359 - val_accuracy: 0.6852 - lr: 0.0010 Epoch 12/1000 14/14 [==============================] - 1s 94ms/step - loss: 0.2238 - accuracy: 0.8819 - val_loss: 0.4404 - val_accuracy: 0.6481 - lr: 0.0010 Epoch 13/1000 14/14 [==============================] - 1s 97ms/step - loss: 0.1954 - accuracy: 0.8912 - val_loss: 0.4215 - val_accuracy: 0.7500 - lr: 0.0010 Epoch 14/1000 14/14 [==============================] - 1s 100ms/step - loss: 0.1792 - accuracy: 0.9051 - val_loss: 0.1971 - val_accuracy: 0.9074 - lr: 0.0010 Epoch 15/1000 14/14 [==============================] - 1s 96ms/step - loss: 0.1608 - accuracy: 0.9144 - val_loss: 0.2836 - val_accuracy: 0.8056 - lr: 0.0010 Epoch 16/1000 14/14 [==============================] - 1s 95ms/step - loss: 0.1447 - accuracy: 0.9398 - val_loss: 0.2867 - val_accuracy: 0.7500 - lr: 0.0010 Epoch 17/1000 14/14 [==============================] - ETA: 0s - loss: 0.1215 - accuracy: 0.9375 Epoch 00017: ReduceLROnPlateau reducing learning rate to 0.0003000000142492354. 14/14 [==============================] - 1s 95ms/step - loss: 0.1215 - accuracy: 0.9375 - val_loss: 0.1474 - val_accuracy: 0.9074 - lr: 0.0010 Epoch 18/1000 14/14 [==============================] - 1s 97ms/step - loss: 0.1023 - accuracy: 0.9537 - val_loss: 0.1186 - val_accuracy: 0.9352 - lr: 3.0000e-04 Epoch 19/1000 14/14 [==============================] - 1s 101ms/step - loss: 0.0992 - accuracy: 0.9606 - val_loss: 0.1074 - val_accuracy: 0.9444 - lr: 3.0000e-04 Epoch 20/1000 14/14 [==============================] - 1s 94ms/step - loss: 0.0837 - accuracy: 0.9676 - val_loss: 0.0917 - val_accuracy: 0.9444 - lr: 3.0000e-04 Epoch 21/1000 14/14 [==============================] - 1s 98ms/step - loss: 0.0788 - accuracy: 0.9699 - val_loss: 0.0877 - val_accuracy: 0.9444 - lr: 3.0000e-04 Epoch 22/1000 14/14 [==============================] - ETA: 0s - loss: 0.0809 - accuracy: 0.9722 Epoch 00022: ReduceLROnPlateau reducing learning rate to 9.000000427477062e-05. 14/14 [==============================] - 1s 95ms/step - loss: 0.0809 - accuracy: 0.9722 - val_loss: 0.0897 - val_accuracy: 0.9444 - lr: 3.0000e-04 Epoch 23/1000 14/14 [==============================] - 1s 95ms/step - loss: 0.0677 - accuracy: 0.9792 - val_loss: 0.0834 - val_accuracy: 0.9537 - lr: 9.0000e-05 Epoch 24/1000 14/14 [==============================] - 1s 93ms/step - loss: 0.0741 - accuracy: 0.9722 - val_loss: 0.0771 - val_accuracy: 0.9537 - lr: 9.0000e-05 Epoch 25/1000 14/14 [==============================] - 1s 94ms/step - loss: 0.0672 - accuracy: 0.9815 - val_loss: 0.0733 - val_accuracy: 0.9537 - lr: 9.0000e-05 Epoch 26/1000 14/14 [==============================] - ETA: 0s - loss: 0.0595 - accuracy: 0.9838 Epoch 00026: ReduceLROnPlateau reducing learning rate to 2.700000040931627e-05. 14/14 [==============================] - 1s 95ms/step - loss: 0.0595 - accuracy: 0.9838 - val_loss: 0.0694 - val_accuracy: 0.9537 - lr: 9.0000e-05 Epoch 27/1000 14/14 [==============================] - 1s 94ms/step - loss: 0.0631 - accuracy: 0.9838 - val_loss: 0.0699 - val_accuracy: 0.9537 - lr: 2.7000e-05 Epoch 28/1000 14/14 [==============================] - 1s 97ms/step - loss: 0.0591 - accuracy: 0.9861 - val_loss: 0.0705 - val_accuracy: 0.9537 - lr: 2.7000e-05 Epoch 29/1000 14/14 [==============================] - ETA: 0s - loss: 0.0635 - accuracy: 0.9838 Epoch 00029: ReduceLROnPlateau reducing learning rate to 8.100000013655517e-06. 14/14 [==============================] - 1s 95ms/step - loss: 0.0635 - accuracy: 0.9838 - val_loss: 0.0697 - val_accuracy: 0.9444 - lr: 2.7000e-05 Epoch 30/1000 14/14 [==============================] - 1s 95ms/step - loss: 0.0643 - accuracy: 0.9792 - val_loss: 0.0687 - val_accuracy: 0.9444 - lr: 8.1000e-06 Epoch 31/1000 14/14 [==============================] - 1s 100ms/step - loss: 0.0768 - accuracy: 0.9745 - val_loss: 0.0665 - val_accuracy: 0.9537 - lr: 8.1000e-06 Epoch 32/1000 14/14 [==============================] - ETA: 0s - loss: 0.0645 - accuracy: 0.9861 Epoch 00032: ReduceLROnPlateau reducing learning rate to 2.429999949526973e-06. 14/14 [==============================] - 1s 95ms/step - loss: 0.0645 - accuracy: 0.9861 - val_loss: 0.0656 - val_accuracy: 0.9537 - lr: 8.1000e-06 Epoch 33/1000 14/14 [==============================] - 1s 97ms/step - loss: 0.0635 - accuracy: 0.9792 - val_loss: 0.0645 - val_accuracy: 0.9630 - lr: 2.4300e-06 Epoch 34/1000 14/14 [==============================] - 1s 95ms/step - loss: 0.0606 - accuracy: 0.9838 - val_loss: 0.0636 - val_accuracy: 0.9630 - lr: 2.4300e-06 Epoch 35/1000 14/14 [==============================] - 1s 95ms/step - loss: 0.0620 - accuracy: 0.9907 - val_loss: 0.0628 - val_accuracy: 0.9630 - lr: 2.4300e-06 Epoch 36/10009/14 [==================>...........] - ETA: 0s - loss: 0.0729 - accuracy: 0.9826WARNING:tensorflow:Can save best model only with val_accuracy available, skipping. 14/14 [==============================] - ETA: 0s - loss: 0.0682 - accuracy: 0.9861 Epoch 00036: ReduceLROnPlateau reducing learning rate to 7.289999985005124e-07. 14/14 [==============================] - 1s 95ms/step - loss: 0.0682 - accuracy: 0.9861 - val_loss: 0.0622 - val_accuracy: 0.9630 - lr: 2.4300e-06 Epoch 37/1000 14/14 [==============================] - 1s 96ms/step - loss: 0.0573 - accuracy: 0.9907 - val_loss: 0.0613 - val_accuracy: 0.9630 - lr: 7.2900e-07 Epoch 38/1000 14/14 [==============================] - 1s 97ms/step - loss: 0.0575 - accuracy: 0.9931 - val_loss: 0.0607 - val_accuracy: 0.9722 - lr: 7.2900e-07 Epoch 39/1000 14/14 [==============================] - 1s 94ms/step - loss: 0.0622 - accuracy: 0.9769 - val_loss: 0.0600 - val_accuracy: 0.9722 - lr: 7.2900e-07 Epoch 40/1000 14/14 [==============================] - 1s 96ms/step - loss: 0.0660 - accuracy: 0.9838 - val_loss: 0.0594 - val_accuracy: 0.9722 - lr: 7.2900e-07 Epoch 41/1000 14/14 [==============================] - ETA: 0s - loss: 0.0614 - accuracy: 0.9884 Epoch 00041: ReduceLROnPlateau reducing learning rate to 2.1870000637136398e-07. 14/14 [==============================] - 1s 95ms/step - loss: 0.0614 - accuracy: 0.9884 - val_loss: 0.0591 - val_accuracy: 0.9722 - lr: 7.2900e-07 Epoch 42/1000 14/14 [==============================] - 1s 94ms/step - loss: 0.0605 - accuracy: 0.9792 - val_loss: 0.0583 - val_accuracy: 0.9722 - lr: 2.1870e-07 Epoch 43/1000 14/14 [==============================] - 1s 99ms/step - loss: 0.0529 - accuracy: 0.9954 - val_loss: 0.0582 - val_accuracy: 0.9722 - lr: 2.1870e-07 Epoch 44/1000 14/14 [==============================] - ETA: 0s - loss: 0.0500 - accuracy: 0.9884 Epoch 00044: ReduceLROnPlateau reducing learning rate to 6.561000276406048e-08. 14/14 [==============================] - 1s 95ms/step - loss: 0.0500 - accuracy: 0.9884 - val_loss: 0.0580 - val_accuracy: 0.9722 - lr: 2.1870e-07 Epoch 45/1000 14/14 [==============================] - 1s 94ms/step - loss: 0.0613 - accuracy: 0.9861 - val_loss: 0.0581 - val_accuracy: 0.9722 - lr: 6.5610e-08 Epoch 46/1000 14/14 [==============================] - 1s 94ms/step - loss: 0.0672 - accuracy: 0.9861 - val_loss: 0.0572 - val_accuracy: 0.9722 - lr: 6.5610e-08 Epoch 47/1000 14/14 [==============================] - ETA: 0s - loss: 0.0511 - accuracy: 0.9931 Epoch 00047: ReduceLROnPlateau reducing learning rate to 1.9683000829218145e-08. 14/14 [==============================] - 1s 96ms/step - loss: 0.0511 - accuracy: 0.9931 - val_loss: 0.0574 - val_accuracy: 0.9722 - lr: 6.5610e-08 Epoch 48/1000 14/14 [==============================] - 1s 99ms/step - loss: 0.0622 - accuracy: 0.9861 - val_loss: 0.0570 - val_accuracy: 0.9722 - lr: 1.9683e-08plt.plot(history.history["accuracy"]) plt.plot(history.history["val_accuracy"]) plt.title("Model?accuracy") plt.ylabel("Accuracy") plt.xlabel("Epoch") plt.legend(["Train",?"Test"],?loc?=?"upper?left") plt.show()plt.plot(history.history["loss"]) plt.plot(history.history["val_loss"]) plt.title("Model?loss") plt.ylabel("Loss") plt.xlabel("Epoch") plt.legend(["Train",?"Test"],?loc?=?"upper?left") plt.show()def?predict_class(img):#?Resizeimg?=?img.reshape(1,128,128,3)#?Predictpredictions?=?model.predict(img)true_prediction?=?[tf.argmax(pred)?for?pred?in?predictions]true_prediction?=?np.array(true_prediction)#?Return?label?corresponding?to?predicted?indexreturn?list(categories.keys())[list(categories.values()).index(true_prediction)]#?Predict?on?test?set y_pred?=?model.predict(X_test)#?From?categorical?outputs?to?discrete?values y_pred_?=?[np.argmax(y)?for?y?in?y_pred] y_test_?=?[np.argmax(y)?for?y?in?y_test]from?sklearn.metrics?import?classification_report print(classification_report(y_test_,?y_pred_))precision recall f1-score support0 1.00 0.96 0.98 251 0.85 1.00 0.92 112 1.00 0.96 0.98 24accuracy 0.97 60macro avg 0.95 0.97 0.96 60 weighted avg 0.97 0.97 0.97 60plt.figure(figsize=(10,10)) random_inds?=?np.random.choice(X_test.shape[0],36) for?i?in?range(36):plt.subplot(6,6,i+1)plt.xticks([])plt.yticks([])plt.grid(False)image_ind?=?random_inds[i]plt.imshow(np.squeeze(X_test[image_ind]),?cmap=plt.cm.binary)#?Predict?and?get?labellabel?=?predict_class(X_test[image_ind])plt.xlabel(label)model.save("model.h5")#?識別接口 def?predict(path,model_str,img_size?=?(128,128)):new_model?=?tf.keras.models.load_model(model_str)img?=?cv2.imread(path)img?=?cv2.resize(img,?img_size)?#?Resize?the?imagesimg?=?np.array(img)????#?Resizeimg?=?img.reshape(1,128,128,3)#?Predictpredictions?=?new_model.predict(img)true_prediction?=?[tf.argmax(pred)?for?pred?in?predictions]true_prediction?=?np.array(true_prediction)#?Return?label?corresponding?to?predicted?indexreturn?list(categories.keys())[list(categories.values()).index(true_prediction)]predict("./data/A/051.jpg","model.h5")'A'predict("./data/B/048.jpg","model.h5")'B'predict("./data/C/050.jpg","model.h5")'C'

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