import pandas as pd import numpy as np import tensorflow as tf import os import matplotlib.pyplot as plt import seaborn as sns import PIL from typing import List from collections import Counter # EfficientNet from tensorflow.keras.applications import EfficientNetB4, ResNet50 from tensorflow.keras.applications.efficientnet import preprocess_input # Data Augmentation from tensorflow.keras.preprocessing.image import ImageDataGenerator # Model Layers from tensorflow.keras import Model, Input from tensorflow.keras.models import Sequential from tensorflow.keras.layers import Dense, Conv2D, MaxPool2D , Flatten, Dropout, BatchNormalization # Compiling and Callbacks from tensorflow.keras.optimizers import SGD,Adam from tensorflow.keras.losses import BinaryCrossentropy from tensorflow.keras.callbacks import ReduceLROnPlateau, ModelCheckpoint, EarlyStopping # Sklearn from sklearn.metrics import roc_auc_score #----------------------------------------------------------------------------------------------------- # Competition Directory comp_dir="/kaggle/input/ranzcr-clip-catheter-line-classification/" # Get Training Data Labels df_train=pd.read_csv(comp_dir+"train.csv").sample(frac=1).reset_index(drop=True) # Get Training/Testing Data Paths test_files = os.listdir(comp_dir+"test") df_test = pd.DataFrame({"StudyInstanceUID": test_files}) image_size = 380 batch_size = 16 num_epochs = 14 learn_rate = 5e-01 df_train.StudyInstanceUID += ".jpg" # Train-Val = [:21000], [21000:], test_files # Train-Val-Test (for tuning model) = [:18000], [18000:24000], [24000:] #----------------------------------------------------------------------------------------------------- label_cols=df_train.columns.tolist() label_cols.remove("StudyInstanceUID") label_cols.remove("PatientID") datagen=ImageDataGenerator() test_datagen=ImageDataGenerator() train_generator=datagen.flow_from_dataframe( dataframe=df_train[:18000], directory=comp_dir+"train", x_col="StudyInstanceUID", y_col=label_cols, batch_size=batch_size, seed=42, shuffle=True, color_mode="rgb", class_mode="raw", target_size=(image_size,image_size), interpolation="bilinear") valid_generator=test_datagen.flow_from_dataframe( dataframe=df_train[18000:24000], directory=comp_dir+"train", x_col="StudyInstanceUID", y_col=label_cols, batch_size=batch_size, seed=42, shuffle=True, color_mode="rgb", class_mode="raw", target_size=(image_size,image_size), interpolation="bilinear") test_generator=test_datagen.flow_from_dataframe( dataframe=df_train[24000:], directory=comp_dir+"train", # Change this x_col="StudyInstanceUID", batch_size=1, seed=42, shuffle=False, color_mode="rgb", class_mode=None, target_size=(image_size,image_size), interpolation="bilinear") #----------------------------------------------------------------------------------------------------- # base_model = ResNet50(include_top=False, # weights=None, # input_shape=(image_size, image_size, 3)) # base_model.load_weights("../input/keras-pretrained-models/resnet50_weights_tf_dim_ordering_tf_kernels_notop.h5", by_name=True) # for layer in base_model.layers[:-6]: # layer.trainable = False base_model = EfficientNetB4(include_top=False, weights="imagenet", input_shape=(image_size, image_size, 3)) # base_model.load_weights("../input/tfkeras-22-pretrained-and-vanilla-efficientnet/TF2.2_EfficientNetB4_NoTop_ImageNet.h5", by_name=True) for layer in base_model.layers[:-6]: if not isinstance(layer, BatchNormalization): layer.trainable = False #----------------------------------------------------------------------------------------------------- inp = Input(shape = (image_size,image_size,3)) x = base_model(inp) x = Flatten()(x) x = Dense(512, activation="relu")(x) output1 = Dense(1, activation = 'sigmoid')(x) output2 = Dense(1, activation = 'sigmoid')(x) output3 = Dense(1, activation = 'sigmoid')(x) output4 = Dense(1, activation = 'sigmoid')(x) output5 = Dense(1, activation = 'sigmoid')(x) output6 = Dense(1, activation = 'sigmoid')(x) output7 = Dense(1, activation = 'sigmoid')(x) output8 = Dense(1, activation = 'sigmoid')(x) output9 = Dense(1, activation = 'sigmoid')(x) output10 = Dense(1, activation = 'sigmoid')(x) output11 = Dense(1, activation = 'sigmoid')(x) model = Model(inp,[output1,output2,output3,output4,output5,output6,output7,output8,output9,output10,output11]) sgd = SGD(lr=learn_rate, momentum=.9, nesterov=False) model.compile(optimizer=sgd, loss = ["binary_crossentropy" for i in range(11)], metrics = ["accuracy"]) def generator_wrapper(generator): for batch_x,batch_y in generator: yield (batch_x,[batch_y[:,i] for i in range(11)]) STEP_SIZE_TRAIN = train_generator.n//train_generator.batch_size STEP_SIZE_VALID = valid_generator.n//valid_generator.batch_size STEP_SIZE_TEST = test_generator.n//test_generator.batch_size # Fit Model history = model.fit(generator_wrapper(train_generator), steps_per_epoch=STEP_SIZE_TRAIN, validation_data=generator_wrapper(valid_generator), validation_steps=STEP_SIZE_VALID, epochs=num_epochs, verbose=2) test_generator.reset() # Get Prediction pred = model.predict(test_generator, steps=STEP_SIZE_TEST, verbose=1) # Get AUC Score # TEST THIS y_pred = np.transpose(np.squeeze(pred)) y_true = df_train.loc[24000:, label_cols].to_numpy() aucs = roc_auc_score(y_true, y_pred, average=None) print("AUC Scores: ", aucs) print("Average AUC: ", np.mean(aucs)) # Create Submission df df_submission = pd.DataFrame() df_submission["StudyInstanceUID"] = test_files df_submission["StudyInstanceUID"] = df_submission["StudyInstanceUID"].map(lambda x: x.replace(".jpg","")) df_preds = pd.DataFrame(np.squeeze(pred)).transpose() df_preds = df_preds.rename(columns=dict(zip([i for i in range(11)], label_cols))) df_submission = pd.concat([df_submission, df_preds], axis=1) df_submission.to_csv("submission.csv", index=False) epochs = range(1,num_epochs) plt.plot(history.history['loss'], label='Training Set') plt.plot(history.history['val_loss'], label='Validation Data)') plt.title('Training and Validation loss') plt.ylabel('MAE') plt.xlabel('Num Epochs') plt.legend(loc="upper left") plt.show() plt.savefig("loss.png")