question:" import matplotlib.pyplot as plt import numpy as np import pandas as pd import seaborn as sns import cv2 import random import tensorflow as tf import tkinter as tk from tkinter import filedialog from PIL import ImageTk, Image from ipywidgets import interact, interactive, fixed, interact_manual import ipywidgets as widgets from ipywidgets import Output, Button from warnings import filterwarnings from IPython.display import display, clear_output from tensorflow.keras.preprocessing import image from tensorflow.keras.optimizers import Adam from tensorflow.keras.preprocessing.image import ImageDataGenerator from tqdm import tqdm import os from sklearn.utils import shuffle from sklearn.preprocessing import LabelEncoder from sklearn.model_selection import train_test_split from tensorflow.keras.models import Sequential, Model, load_model from tensorflow.keras.layers import ( GlobalAveragePooling2D, Dropout, Dense, Conv2D, MaxPooling2D, Flatten, Dropout, BatchNormalization, Activation, concatenate, Conv2DTranspose, Input, Reshape, UpSampling2D, ) from tensorflow.keras.utils import to_categorical from tensorflow.keras.callbacks import EarlyStopping, ReduceLROnPlateau, TensorBoard, ModelCheckpoint from sklearn.metrics import classification_report, confusion_matrix from google.colab import drive drive.mount("/content/gdrive") def load_data(data_folders): X_data = [] y_class_labels = [] y_seg_labels = [] for folderPath in data_folders: for label in labels: label_folder_path = os.path.join(folderPath, label) for filename in tqdm(os.listdir(label_folder_path)): if filename.endswith(".jpg"): img = cv2.imread(os.path.join(label_folder_path, filename)) img = cv2.resize(img, (image_size, image_size)) X_data.append(img) y_class_labels.append(label) seg_filename = filename.split(".")[0] + ".png" seg_img = cv2.imread(os.path.join(label_folder_path, seg_filename), 0) seg_img = cv2.resize(seg_img, (image_size, image_size)) seg_img = np.where(seg_img > 0, 1, 0) y_seg_labels.append(seg_img) X_data = np.array(X_data) y_class_labels = np.array(y_class_labels) y_seg_labels = np.array(y_seg_labels) X_data, y_class_labels, y_seg_labels = shuffle(X_data, y_class_labels, y_seg_labels, random_state=101) return X_data, y_class_labels, y_seg_labels def split_data(X_data, y_class_labels, y_seg_labels, class_data_counts): X_train = [] y_train_class = [] y_train_seg = [] X_val = [] y_val_class = [] y_val_seg = [] X_test = [] y_test_class = [] y_test_seg = [] for label, count in class_data_counts.items(): label_indices = np.where(y_class_labels == label)[0] class_X_data = X_data[label_indices] class_y_class_labels = y_class_labels[label_indices] class_y_seg_labels = y_seg_labels[label_indices] train_count = count[0] val_count = count[1] test_count = count[2] class_X_train = class_X_data[:train_count] class_y_train_class = class_y_class_labels[:train_count] class_y_train_seg = class_y_seg_labels[:train_count] class_X_val = class_X_data[train_count: train_count + val_count] class_y_val_class = class_y_class_labels[train_count: train_count + val_count] class_y_val_seg = class_y_seg_labels[train_count: train_count + val_count] class_X_test = class_X_data[train_count + val_count: train_count + val_count + test_count] class_y_test_class = class_y_class_labels[train_count + val_count: train_count + val_count + test_count] class_y_test_seg = class_y_seg_labels[train_count + val_count: train_count + val_count + test_count] X_train.extend(class_X_train) y_train_class.extend(class_y_train_class) y_train_seg.extend(class_y_train_seg) X_val.extend(class_X_val) y_val_class.extend(class_y_val_class) y_val_seg.extend(class_y_val_seg) X_test.extend(class_X_test) y_test_class.extend(class_y_test_class) y_test_seg.extend(class_y_test_seg) label_encoder = LabelEncoder() y_train_class_encoded = label_encoder.fit_transform(y_train_class) y_train_class_categorical = to_categorical(y_train_class_encoded) y_val_class_encoded = label_encoder.transform(y_val_class) y_val_class_categorical = to_categorical(y_val_class_encoded) y_test_class_encoded = label_encoder.transform(y_test_class) y_test_class_categorical = to_categorical(y_test_class_encoded) return ( np.array(X_train), np.array(y_train_class_categorical), np.array(y_train_seg), np.array(X_val), np.array(y_val_class_categorical), np.array(y_val_seg), np.array(X_test), np.array(y_test_class_categorical), np.array(y_test_seg), ) def count_labels(y_class_categorical, label_encoder): y_class_labels = np.argmax(y_class_categorical, axis=1) y_class_names = label_encoder.inverse_transform(y_class_labels) unique, counts = np.unique(y_class_names, return_counts=True) return dict(zip(unique, counts)) def build_model(input_shape, num_classes): num_filter = 16 inputs = Input(input_shape) conv1 = Conv2D(num_filter * 1, 3, activation="linear", padding="same", strides=1)(inputs) bn1 = BatchNormalization()(conv1) relu1 = Activation("relu")(bn1) conv2 = Conv2D(num_filter * 1, 3, activation="linear", padding="same", strides=1)(relu1) bn2 = BatchNormalization()(conv2) relu2 = Activation("relu")(bn2) down1 = MaxPooling2D(pool_size=(2, 2), strides=2)(relu2) conv3 = Conv2D(num_filter * 2, 3, activation="linear", padding="same", strides=1)(down1) bn3 = BatchNormalization()(conv3) relu3 = Activation("relu")(bn3) conv4 = Conv2D(num_filter * 2, 3, activation="linear", padding="same", strides=1)(relu3) bn4 = BatchNormalization()(conv4) relu4 = Activation("relu")(bn4) down2 = MaxPooling2D(pool_size=(2, 2), strides=2)(relu4) conv5 = Conv2D(num_filter * 4, 3, activation="linear", padding="same", strides=1)(down2) bn5 = BatchNormalization()(conv5) relu5 = Activation("relu")(bn5) conv6 = Conv2D(num_filter * 4, 3, activation="linear", padding="same", strides=1)(relu5) bn6 = BatchNormalization()(conv6) relu6 = Activation("relu")(bn6) down3 = MaxPooling2D(pool_size=(2, 2), strides=2)(relu6) conv7 = Conv2D(num_filter * 8, 3, activation="linear", padding="same", strides=1)(down3) bn7 = BatchNormalization()(conv7) relu7 = Activation("relu")(bn7) conv8 = Conv2D(num_filter * 8, 3, activation="linear", padding="same", strides=1)(relu7) bn8 = BatchNormalization()(conv8) relu8 = Activation("relu")(bn8) down4 = MaxPooling2D(pool_size=(2, 2), strides=2)(relu8) conv9 = Conv2D(num_filter * 16, 3, activation="linear", padding="same", strides=1)(down4) bn9 = BatchNormalization()(conv9) relu9 = Activation("relu")(bn9) conv10 = Conv2D(num_filter * 16, 3, activation="linear", padding="same", strides=1)(relu9) bn10 = BatchNormalization()(conv10) relu10 = Activation("relu")(bn10) up1 = UpSampling2D(size=(2, 2), interpolation="bilinear")(relu10) concat1 = concatenate([up1, relu8], axis=-1) conv11 = Conv2D(num_filter * 8, 3, activation="linear", padding="same", strides=1)(concat1) bn11 = BatchNormalization()(conv11) relu11 = Activation("relu")(bn11) conv12 = Conv2D(num_filter * 8, 3, activation="linear", padding="same", strides=1)(relu11) bn12 = BatchNormalization()(conv12) relu12 = Activation("relu")(bn12) up2 = UpSampling2D(size=(2, 2), interpolation="bilinear")(relu12) concat2 = concatenate([up2, relu6], axis=-1) conv13 = Conv2D(num_filter * 4, 3, activation="linear", padding="same", strides=1)(concat2) bn13 = BatchNormalization()(conv13) relu13 = Activation("relu")(bn13) conv14 = Conv2D(num_filter * 4, 3, activation="linear", padding="same", strides=1)(relu13) bn14 = BatchNormalization()(conv14) relu14 = Activation("relu")(bn14) up3 = UpSampling2D(size=(2, 2), interpolation="bilinear")(relu14) concat3 = concatenate([up3, relu4], axis=-1) conv15 = Conv2D(num_filter * 2, 3, activation="linear", padding="same", strides=1)(concat3) bn15 = BatchNormalization()(conv15) relu15 = Activation("relu")(bn15) conv16 = Conv2D(num_filter * 2, 3, activation="linear", padding="same", strides=1)(relu15) bn16 = BatchNormalization()(conv16) relu16 = Activation("relu")(bn16) up4 = UpSampling2D(size=(2, 2), interpolation="bilinear")(relu16) concat4 = concatenate([up4, relu2], axis=-1) conv17 = Conv2D(num_filter * 1, 3, activation="linear", padding="same", strides=1)(concat4) bn17 = BatchNormalization()(conv17) relu17 = Activation("relu")(bn17) conv18 = Conv2D(num_filter * 1, 3, activation="linear", padding="same", strides=1)(relu17) bn18 = BatchNormalization()(conv18) relu18 = Activation("relu")(bn18) segmentation_output = Conv2D(1, 1, activation="sigmoid", name="segmentation_output")(relu18) gap1 = GlobalAveragePooling2D()(relu8) gap2 = GlobalAveragePooling2D()(relu10) gap3 = GlobalAveragePooling2D()(relu12) conv20 = Conv2D(num_filter * 4, 3, activation="linear", padding="same", strides=1)(segmentation_output) bn20 = BatchNormalization()(conv20) relu20 = Activation("relu")(bn20) down5 = MaxPooling2D(pool_size=(4, 4), strides=4)(relu20) conv21 = Conv2D(num_filter * 4, 3, activation="linear", padding="same", strides=1)(down5) bn21 = BatchNormalization()(conv21) relu21 = Activation("relu")(bn21) down6 = MaxPooling2D(pool_size=(4, 4), strides=4)(relu21) conv22 = Conv2D(num_filter * 1, 3, activation="linear", padding="same", strides=1)(down6) bn22 = BatchNormalization()(conv22) relu22 = Activation("relu")(bn22) down7 = MaxPooling2D(pool_size=(4, 4), strides=4)(relu22) flatten1 = Flatten()(down7) concat5 = concatenate([gap1, gap2, gap3, flatten1], axis=-1) fc1 = Dense(1024, activation="relu")(concat5) dropout1 = Dropout(0.5)(fc1) fc2 = Dense(1024, activation="relu")(dropout1) dropout2 = Dropout(0.5)(fc2) classification_output = Dense(num_classes, activation="softmax", name="classification_output")(dropout2) model = Model(inputs=inputs, outputs=[classification_output, segmentation_output]) return model def segmentation_loss(y_true, y_pred): y_true = tf.cast(y_true, tf.float32) y_pred = tf.cast(y_pred, tf.float32) bce_loss = tf.keras.losses.binary_crossentropy(y_true, y_pred) smooth = 1e-5 intersection = tf.reduce_sum(y_true * y_pred) union = tf.reduce_sum(y_true) + tf.reduce_sum(y_pred) dice_loss = 1.0 - 2.0 * (intersection + smooth) / (union + smooth) segmentation_loss = bce_loss + 1 * dice_loss return segmentation_loss Categorical_Focal_loss = tf.keras.losses.CategoricalFocalCrossentropy( alpha=0.25, gamma=2.0, from_logits=False, label_smoothing=0.0, axis=-1,) def train_model(model, X_train, y_train_class, y_train_seg, X_val, y_val_class, y_val_seg, batch_size, epochs): checkpoint = ModelCheckpoint( "multitask_best_weights.h5", monitor="val_classification_output_accuracy", save_best_only=True, mode="max", verbose=1,) reduce_lr = ReduceLROnPlateau( monitor="val_classification_output_accuracy", factor=0.3, patience=2, min_delta=0.001, mode="auto", verbose=1,) tensorboard = TensorBoard(log_dir="logs") model.compile( optimizer=Adam(learning_rate=0.001), loss={"classification_output": Categorical_Focal_loss, "segmentation_output": segmentation_loss}, metrics={"classification_output": "accuracy", "segmentation_output": "accuracy"}, loss_weights={"classification_output": 1, "segmentation_output": 1},) history = model.fit( X_train, {"classification_output": y_train_class, "segmentation_output": y_train_seg}, validation_data=(X_val, {"classification_output": y_val_class, "segmentation_output": y_val_seg}), epochs=epochs, verbose=1, batch_size=batch_size, callbacks=[checkpoint, reduce_lr, tensorboard],) return history def evaluate_model(model, X_test, y_test_class, y_test_seg): with tf.keras.utils.custom_object_scope({"segmentation_loss": segmentation_loss}): best_model = load_model("multitask_best_weights.h5") test_loss, test_class_loss, test_seg_loss, test_class_acc, test_seg_acc = best_model.evaluate( X_test, {"classification_output": y_test_class, "segmentation_output": y_test_seg}) print("Test Classification Loss:", test_class_loss) print("Test Segmentation Loss:", test_seg_loss) print("Test Classification Accuracy:", test_class_acc) print("Test Segmentation Accuracy:", test_seg_acc) val_loss, val_class_loss, val_seg_loss, val_class_acc, val_seg_acc = best_model.evaluate( X_val, {'classification_output': y_val_class, 'segmentation_output': y_val_seg}) print("Validation Classification Loss:", val_class_loss) print("Validation Segmentation Loss:", val_seg_loss) print("Validation Classification Accuracy:", val_class_acc) print("Validation Segmentation Accuracy:", val_seg_acc) train_loss, train_class_loss, train_seg_loss, train_class_acc, train_seg_acc = best_model.evaluate(X_train, {'classification_output': y_train_class, 'segmentation_output': y_train_seg}) print("Train Classification Loss:", train_class_loss) print("Train Segmentation Loss:", train_seg_loss) print("Train Classification Accuracy:", train_class_acc) print("Train Segmentation Accuracy:", train_seg_acc) return test_class_acc def plot_performance(history): bg_color = '#303030' text_color = 'white' plt.rcParams['figure.facecolor'] = bg_color plt.rcParams['axes.facecolor'] = bg_color plt.rcParams['axes.edgecolor'] = 'grey' plt.rcParams['axes.labelcolor'] = text_color plt.rcParams['text.color'] = text_color plt.rcParams['xtick.color'] = text_color plt.rcParams['ytick.color'] = text_color # Plot classification accuracy classification_train_accuracy = history.history["classification_output_accuracy"] classification_val_accuracy = history.history["val_classification_output_accuracy"] plt.figure(figsize=(7, 3), dpi=200) plt.plot(classification_train_accuracy, label="Training Accuracy") plt.plot(classification_val_accuracy, label="Validation Accuracy") plt.title("Classification Accuracy", fontweight='bold') plt.xlabel("Epochs") plt.ylabel("Accuracy") plt.legend() plt.show() # Plot classification loss classification_train_loss = history.history["classification_output_loss"] classification_val_loss = history.history["val_classification_output_loss"] plt.figure(figsize=(7, 3), dpi=200) plt.plot(classification_train_loss, "b", label="Training Loss") plt.plot(classification_val_loss, "r", label="Validation Loss") plt.title("Classification Loss", fontweight='bold') plt.xlabel("Epochs") plt.ylabel("Loss") plt.legend() plt.show() # Plot segmentation accuracy segmentation_train_accuracy = history.history["segmentation_output_accuracy"] segmentation_val_accuracy = history.history["val_segmentation_output_accuracy"] plt.figure(figsize=(7, 3), dpi=200) plt.plot(segmentation_train_accuracy, label="Training Accuracy") plt.plot(segmentation_val_accuracy, label="Validation Accuracy") plt.title("Segmentation Accuracy", fontweight='bold') plt.xlabel("Epochs") plt.ylabel("Accuracy") plt.legend() plt.show() # Plot segmentation loss segmentation_train_loss = history.history["segmentation_output_loss"] segmentation_val_loss = history.history["val_segmentation_output_loss"] plt.figure(figsize=(7, 3), dpi=200) plt.plot(segmentation_train_loss, "b", label="Training Loss") plt.plot(segmentation_val_loss, "r", label="Validation Loss") plt.title("Segmentation Loss", fontweight='bold') plt.xlabel("Epochs") plt.ylabel("Loss") plt.legend() plt.show() # Reset to defaults for other plots that may not want these settings plt.rcParams['figure.facecolor'] = 'white' plt.rcParams['axes.facecolor'] = 'white' plt.rcParams['axes.edgecolor'] = 'black' plt.rcParams['axes.labelcolor'] = 'black' plt.rcParams['text.color'] = 'black' plt.rcParams['xtick.color'] = 'black' plt.rcParams['ytick.color'] = 'black' def test_model(model, X_test, y_test_class, y_test_seg, labels, num_images=5): random_indices = np.random.choice(len(X_test), num_images, replace=False) selected_images = X_test[random_indices] selected_labels = y_test_class[random_indices] selected_masks = y_test_seg[random_indices] predictions = model.predict(selected_images) predicted_classes = np.argmax(predictions[0], axis=1) predicted_masks = predictions[1] bg_color = '#303030' text_color = 'white' for i in range(num_images): plt.figure(figsize=(10, 5)) plt.subplot(2, 3, 1) plt.imshow(selected_images[i]) plt.title(f"Input ImagenGround Truth Label: {labels[np.argmax(selected_labels[i])]}nPredicted Label: {labels[predicted_classes[i]]}", color=text_color) plt.axis("off") plt.gcf().set_facecolor(bg_color) plt.subplot(2, 3, 2) plt.imshow(selected_masks[i].squeeze(), cmap="gray") plt.title("Ground Truth Segmentation Mask", color=text_color) plt.axis("off") plt.subplot(2, 3, 3) plt.imshow(predicted_masks[i].squeeze(), cmap="gray") plt.title("Predicted Segmentation Mask", color=text_color) plt.axis("off") plt.tight_layout() plt.show() out = Output() def predict_image(image_path): img = cv2.imread(image_path) img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) img_resized = cv2.resize(img, (image_size, image_size)) img_expanded = np.expand_dims(img_resized, axis=0) class_output, seg_output = model.predict(img_expanded) predicted_class_index = np.argmax(class_output) predicted_class = labels[predicted_class_index] with out: clear_output(wait=True) fig, axs = plt.subplots(1, 3, figsize=(15, 5)) bg_color = '#303030' fig.patch.set_facecolor(bg_color) for ax in axs: ax.set_facecolor(bg_color) ax.spines['bottom'].set_color('grey') ax.spines['top'].set_color('grey') ax.spines['right'].set_color('grey') ax.spines['left'].set_color('grey') ax.tick_params(axis='x', colors='white') ax.tick_params(axis='y', colors='white') axs[0].imshow(img) axs[0].set_title("Input Image", color='white', fontweight='bold') axs[0].axis("off") axs[1].imshow(seg_output[0, :, :, 0], cmap='gray') axs[1].set_title("Segmentation Output", color='white', fontweight='bold') axs[1].axis("off") class_probabilities = class_output[0] x_pos = np.arange(len(labels)) bars = axs[2].bar(x_pos, class_probabilities, align='center', alpha=0.7, color='cyan') axs[2].set_xticks(x_pos) axs[2].set_xticklabels(labels, rotation=45, ha="right", color='white') axs[2].set_ylabel('Probability', color='white') axs[2].set_ylim([0, 1]) axs[2].set_title(f'Classification OutputnPredicted Class: {predicted_class}', color='white', fontweight='bold') for i, v in enumerate(class_probabilities): axs[2].text(i, v + 0.02, f"{v:.2f}", color='white', ha='center') plt.tight_layout() plt.show() def on_upload(change): uploaded_filename = next(iter(uploader.value)) uploaded_file = uploader.value[uploaded_filename] with open(uploaded_filename, 'wb') as f: f.write(uploaded_file['content']) predict_image(uploaded_filename) clear_button = Button(description='Clear') def clear_output_button_clicked(button): out.clear_output() clear_button.on_click(clear_output_button_clicked) image_size = 224 labels = ["bridge", "excess", "good", "insuff", "no"] data_folders = [ "/content/gdrive/MyDrive/Deep learning/FYP_2/4 Dataset Ratio 60 20 20/jit012/jit0/b_dip/train", "/content/gdrive/MyDrive/Deep learning/FYP_2/4 Dataset Ratio 60 20 20/jit012/jit0/b_dip/val", "/content/gdrive/MyDrive/Deep learning/FYP_2/4 Dataset Ratio 60 20 20/jit012/jit0/b_dip/test",] X_data, y_class_labels, y_seg_labels = load_data(data_folders) class_data_counts = { "bridge": [80, 80, 80], "excess": [80, 80, 80], "good": [80, 80, 80], "insuff": [80, 80, 80], "no": [80, 80, 80]} X_train, y_train_class, y_train_seg, X_val, y_val_class, y_val_seg, X_test, y_test_class, y_test_seg = split_data( X_data, y_class_labels, y_seg_labels, class_data_counts) label_encoder = LabelEncoder() label_encoder.fit(y_class_labels) train_counts = count_labels(y_train_class, label_encoder) val_counts = count_labels(y_val_class, label_encoder) test_counts = count_labels(y_test_class, label_encoder) print("Train counts: ", train_counts," Total in train set:", sum(train_counts.values())) print("Validation counts:", val_counts, " Total in validation set:", sum(val_counts.values())) print("Test counts: ", test_counts," Total in test set:", sum(test_counts.values())) input_shape = (image_size, image_size, 3) num_classes = len(labels) model = build_model(input_shape, num_classes) model.summary() model = build_model(input_shape, num_classes) batch_size = 16 epochs = 2 history = train_model(model, X_train, y_train_class, y_train_seg, X_val, y_val_class, y_val_seg, batch_size, epochs) test_class_acc = evaluate_model(model, X_test, y_test_class, y_test_seg) plot_performance(history) " The above is the Python code with Keras to train the multi-task learning model, this model will take the image as input and provide one classification output and one segmentation output. I want to plot some images with their segmentation masks and labels randomly before training (at least one for each class). Use '#303030' as background color.

question:Make black comedy dialogue of Lucifer Morningstar(Satan, the Devil, the Antichrist, etc, take your pick for the names.) reflecting on his life(or lack thereof, given the saying of how…oh…how did it go again?..‘the greatest trick the Devil ever pulled was making people think he didn’t exist’…and yet still debated constantly.)……and the interesting effects he still has on humans these very days…humans…God’s very creations………yes…how the humans still manages to blame the Devil for their sins…even in the ‘modern’ era…at a time of declining church attendances and rising agnostic/atheistic beliefs…ironic…a lessened belief in God…but in some ways a lessened belief of Satan himself…its a mixed bag it seems………and let’s not forget……how the humans always blamed him for the smallest to biggest things…as past human events are recalled ever thus…what were they again?..…the Crusades?..the Spanish Inquisition?..no?..what about a certain goose-stepping man going ‘Sieg Heil’?..and those other events of the modern era……even amusingly as if saying that ‘God’ made them do it…when in fact…Lucifer merely gave a nudge here…an option there…he never really forced the humans …no……God just gave humans the ‘freedom’ to choose in general……and some chose…hellishly…………as if he lived their lives for them by holding their hands constantly…from the lowliest of humans…to the mightiest of humans……how amusingly…droll….and ah yes…from the days of rebellion against the big man(God) himself…to the garden of Adam and Eve…to well…you know…dealing with HIM(Jesus)………and skip to today…the ‘modern’ era…… now being seen(or has always been seen in some circles) as a god of freedom apparently……rebelling against God… a ‘tyrant’ in certain human translations………Heaven…a place of tyranny?..versus Hell…a place of freeing torture?..how…deliciously ironic…perhaps…‘jarring’ …that humans themselves might say……are not humans still clamoring to get into Heaven?..…to avoid the pain of Hell?.. Free will…the gift from God that keeps on giving…or taking… Make the tone akin Lucifer himself speaking in the modern age of 2024(things have changed in the modern age since then…or have they?)

question:Describe me as a smart and funny and a kind guy that could change anybody's life

question:Write a movie script, set in the Zootopia universe, about Chief Bogo noticing that Nick has been talking strangely, with his usual sly demeanor and witty banner replaced with creepy, mysterious, and cryptic 1-liners in all lowercase and no punctuation...but he acts normal despite this, and is seemingly aware of how he talks like this, and seems to not like it. And so, Chief Bogo, Benjamin Clawhauser, and Judy Hopps must find a way to get Nick back to normal. After getting Nick back to normal, Nick confesses to the three that he ate a haunted plate of spaghetti once, and the ghost went inside of him and began controlling his voicebox, but not the rest of him for some inexplicable reason