Pseudocode ResNet50

Input: Chest Radiographs Output: classification results: Covid Or Normal Start   lr ← 1 × 10−4           ▷ lr is Initial_Learning_rate   Batch_Size ← 32   Number_of_Epochs ← 28   Base_Model ← ResNet50(weights ← “imagenet”, include_top ← False,   input_tensor ← Input (shape ← (224, 224, 3)))  ▷ ResNet50 is the base Model   headModel ← baseModel.output   headModel ← AveragePooling2D(pool_size ← (7, 7))(headModel)    headModel ← Flatten(name ← “flatten”)(headModel)   headModel ← Dense(256, activation ← “relu”)(headModel)   headModel ← Dropout(0.5)(headModel)   headModel ← Dense(len(CLASSES), activation ← “softmax”)(headModel)   model ← Model(inputs ← baseModel.input, outputs ← headModel)   for layer in baseModel.layers:       layer.trainable ← True   end for   opt ← optimizers.Adam (lr ← INIT_LR, decay ← INIT_LR/Number_of_Epochs)   model.compile (loss ← “binary_crossentropy”, optimizer ← opt, metrics ← [“Accuracy”])   H ← model.fit_generator (trainGen, steps_per_epoch ← totalTrain, validation_data ← valGen, validation_steps ← totalVal, epochs ← Number_of_Epochs) End