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Ronjinson981 / blindfaith.app

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DeepL #6

Open Ronjinson981 opened 3 months ago

Ronjinson981 commented 3 months ago

Importing essential libraries for deep learning

Numerical operations

import numpy as np

Data manipulation and analysis

import pandas as pd

Data visualization

import matplotlib.pyplot as plt import seaborn as sns

Deep learning frameworks

import tensorflow as tf from tensorflow import keras import torch

Machine learning tools

from sklearn.model_selection import train_test_split from sklearn.preprocessing import StandardScaler, LabelEncoder

Image processing

import cv2

Natural language processing

import nltk import spacy

Ensure that the NLTK data is downloaded

nltk.download('punkt') nltk.download('stopwords')

Load spaCy's English model

nlp = spacy.load('en_core_web_sm')

Importing essential libraries for deep learning

Numerical operations

import numpy as np

Data manipulation and analysis

import pandas as pd

Data visualization

import matplotlib.pyplot as plt import seaborn as sns

Deep learning frameworks

import tensorflow as tf from tensorflow import keras import torch import torch.nn as nn import torch.optim as optim

Machine learning tools

from sklearn.model_selection import train_test_split from sklearn.preprocessing import StandardScaler, LabelEncoder

Image processing

import cv2

Natural language processing

import nltk import spacy

Ensure that the NLTK data is downloaded

nltk.download('punkt') nltk.download('stopwords')

Load spaCy's English model

nlp = spacy.load('en_core_web_sm')

Example of loading a dataset

def load_data(file_path): data = pd.read_csv(file_path) return data

Example of preprocessing data

def preprocess_data(data):

Handling missing values

data = data.dropna()

# Encoding categorical variables
label_encoder = LabelEncoder()
data['category'] = label_encoder.fit_transform(data['category'])

# Standardizing numerical features
scaler = StandardScaler()
data[['feature1', 'feature2']] = scaler.fit_transform(data[['feature1', 'feature2']])

return data

Example of building a simple neural network with Keras

def build_keras_model(input_shape): model = keras.Sequential([ keras.layers.Dense(64, activation='relu', input_shape=input_shape), keras.layers.Dense(64, activation='relu'), keras.layers.Dense(1, activation='sigmoid') ])

model.compile(optimizer='adam',
              loss='binary_crossentropy',
              metrics=['accuracy'])

return model

Example of building a simple neural network with PyTorch

class SimpleNN(nn.Module): def init(self, input_dim): super(SimpleNN, self).init() self.fc1 = nn.Linear(input_dim, 64) self.fc2 = nn.Linear(64, 64) self.fc3 = nn.Linear(64, 1)

def forward(self, x):
    x = torch.relu(self.fc1(x))
    x = torch.relu(self.fc2(x))
    x = torch.sigmoid(self.fc3(x))
    return x

Example of training a Keras model

def train_keras_model(model, X_train, y_train, X_val, y_val, epochs=10, batch_size=32): history = model.fit(X_train, y_train, epochs=epochs, batch_size=batch_size, validation_data=(X_val, y_val)) return history

Example of training a PyTorch model

def train_pytorch_model(model, train_loader, val_loader, epochs=10, learning_rate=0.001): criterion = nn.BCELoss() optimizer = optim.Adam(model.parameters(), lr=learning_rate)

for epoch in range(epochs):
    model.train()
    for inputs, targets in train_loader:
        outputs = model(inputs)
        loss = criterion(outputs, targets)
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()

    model.eval()
    val_loss = 0
    with torch.no_grad():
        for inputs, targets in val_loader:
            outputs = model(inputs)
            val_loss += criterion(outputs, targets).item()

    print(f'Epoch {epoch+1}/{epochs}, Validation Loss: {val_loss/len(val_loader)}')

Example of evaluating a Keras model

def evaluate_keras_model(model, X_test, y_test): test_loss, test_acc = model.evaluate(X_test, y_test) print(f'Test accuracy: {test_acc:.4f}') return test_loss, test_acc

Example of evaluating a PyTorch model

def evaluate_pytorch_model(model, test_loader): model.eval() correct = 0 total = 0 with torch.no_grad(): for inputs, targets in test_loader: outputs = model(inputs) predicted = (outputs > 0.5).float() total += targets.size(0) correct += (predicted == targets).sum().item()

accuracy = correct / total
print(f'Test accuracy: {accuracy:.4f}')
return accuracy

Function to load and preprocess image data

def load_image_data(image_paths, image_size): images = [] for img_path in image_paths: img = cv2.imread(img_path) img = cv2.resize(img, image_size) img = img / 255.0 # Normalize to [0, 1] images.append(img) return np.array(images)

Function to tokenize and preprocess text data using NLTK

def preprocess_text_nltk(texts): stop_words = set(nltk.corpus.stopwords.words('english')) tokenizer = nltk.tokenize.RegexpTokenizer(r'\w+') processed_texts = []

for text in texts:
    tokens = tokenizer.tokenize(text)
    tokens = [word.lower() for word in tokens if word.lower() not in stop_words]
    processed_texts.append(tokens)

return processed_texts

Function to preprocess text data using spaCy

def preprocess_text_spacy(texts): processed_texts = []

for text in texts:
    doc = nlp(text)
    tokens = [token.lemma_ for token in doc if not token.is_stop and not token.is_punct]
    processed_texts.append(tokens)

return processed_texts

Function to save a Keras model

def save_keras_model(model, file_path): model.save(file_path) print(f"Model saved to {file_path}")

Function to load a Keras model

def load_keras_model(file_path): model = keras.models.load_model(file_path) print(f"Model loaded from {file_path}") return model

Function to save a PyTorch model

def save_pytorch_model(model, file_path): torch.save(model.state_dict(), file_path) print(f"Model saved to {file_path}")

Function to load a PyTorch model

def load_pytorch_model(model_class, input_dim, file_path): model = model_class(input_dim) model.load_state_dict(torch.load(file_path)) model.eval() print(f"Model loaded from {file_path}") return model

Example usage

if name == "main":

Assuming you have a CSV file with data

data = load_data('data.csv')
processed_data = preprocess_data(data)

# Split data into features and labels
X = processed_data.drop('label', axis=1).values
y = processed_data['label'].values

# Split data into training, validation, and test sets
X_train, X_temp, y_train, y_temp = train_test_split(X, y, test_size=0.3, random_state=42)
X_val, X_test, y_val, y_test = train_test_split(X_temp, y_temp, test_size=0.5, random_state=42)

# Build and train a Keras model
keras_model = build_keras_model((X_train.shape[1],))
train_keras_model(keras_model, X_train, y_train, X_val, y_val)

# Evaluate the Keras model
evaluate_keras_model(keras_model, X_test, y_test)

# Save the Keras model
save_keras_model(keras_model, 'keras_model.h5')

# Load the Keras model
keras_model_loaded = load_keras_model('keras_model.h5')

# Build and train a PyTorch model
pytorch_model = SimpleNN(X_train.shape[1])

# Assuming you have DataLoader for train, validation, and test sets
# train_loader, val_loader, test_loader = create_data_loaders(X_train, y_train, X_val, y_val, X_test, y_test)

# Train the PyTorch model
# train_pytorch_model(pytorch_model, train_loader, val_loader)

# Evaluate the PyTorch model
# evaluate_pytorch_model(pytorch_model, test_loader)

# Save the PyTorch model
save_pytorch_model(pytorch_model, 'pytorch_model.pth')

# Load the PyTorch model
pytorch_model_loaded = load_pytorch_model(SimpleNN, X_train.shape[1], 'pytorch_model.pth')

# Load and preprocess image data
# image_paths = ['path/to/image1.jpg', 'path/to/image2.jpg']
# images = load_image_data(image_paths, (128, 128))

# Preprocess text data
# texts = ["This is a sample text.", "Another example sentence."]
# processed_texts_nltk = preprocess_text_nltk(texts)
# processed_texts_spacy = preprocess_text_spacy(texts)

from torch.utils.data import DataLoader, Dataset from torchvision import transforms from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint import os

Custom PyTorch Dataset

class CustomDataset(Dataset): def init(self, data, labels, transform=None): self.data = data self.labels = labels self.transform = transform

def __len__(self):
    return len(self.data)

def __getitem__(self, idx):
    sample = self.data[idx]
    label = self.labels[idx]
    if self.transform:
        sample = self.transform(sample)
    return sample, label

Function to create DataLoaders for PyTorch

def create_data_loaders(X_train, y_train, X_val, y_val, X_test, y_test, batch_size=32): transform = transforms.Compose([transforms.ToTensor()])

train_dataset = CustomDataset(X_train, y_train, transform=transform)
val_dataset = CustomDataset(X_val, y_val, transform=transform)
test_dataset = CustomDataset(X_test, y_test, transform=transform)

train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)
val_loader = DataLoader(val_dataset, batch_size=batch_size, shuffle=False)
test_loader = DataLoader(test_dataset, batch_size=batch_size, shuffle=False)

return train_loader, val_loader, test_loader

Data augmentation for image data using Keras

def augment_image_data(X_train): datagen = keras.preprocessing.image.ImageDataGenerator( rotation_range=20, width_shift_range=0.2, height_shift_range=0.2, shear_range=0.2, zoom_range=0.2, horizontal_flip=True, fill_mode='nearest' ) datagen.fit(X_train) return datagen

Implementing callbacks for Keras

def get_keras_callbacks(model_path): early_stopping = EarlyStopping(monitor='val_loss', patience=10, restore_best_weights=True) model_checkpoint = ModelCheckpoint(model_path, monitor='val_loss', save_best_only=True) return [early_stopping, model_checkpoint]

Plotting training history

def plot_training_history(history): plt.figure(figsize=(12, 4))

# Plotting accuracy
plt.subplot(1, 2, 1)
plt.plot(history.history['accuracy'], label='train_accuracy')
plt.plot(history.history['val_accuracy'], label='val_accuracy')
plt.xlabel('Epochs')
plt.ylabel('Accuracy')
plt.legend()
plt.title('Model Accuracy')

# Plotting loss
plt.subplot(1, 2, 2)
plt.plot(history.history['loss'], label='train_loss')
plt.plot(history.history['val_loss'], label='val_loss')
plt.xlabel('Epochs')
plt.ylabel('Loss')
plt.legend()
plt.title('Model Loss')

plt.show()

Example usage

if name == "main":

Assuming you have a CSV file with data

data = load_data('data.csv')
processed_data = preprocess_data(data)

# Split data into features and labels
X = processed_data.drop('label', axis=1).values
y = processed_data['label'].values

# Split data into training, validation, and test sets
X_train, X_temp, y_train, y_temp = train_test_split(X, y, test_size=0.3, random_state=42)
X_val, X_test, y_val, y_test = train_test_split(X_temp, y_temp, test_size=0.5, random_state=42)

# Build and train a Keras model
keras_model = build_keras_model((X_train.shape[1],))

# Implement callbacks for Keras
keras_callbacks = get_keras_callbacks('best_keras_model.h5')

# Train the Keras model with callbacks
history = train_keras_model(keras_model, X_train, y_train, X_val, y_val, epochs=50, batch_size=32, callbacks=keras_callbacks)

# Plot training history
plot_training_history(history)

# Evaluate the Keras model
evaluate_keras_model(keras_model, X_test, y_test)

# Save the Keras model
save_keras_model(keras_model, 'keras_model.h5')

# Load the Keras model
keras_model_loaded = load_keras_model('keras_model.h5')

# Build and train a PyTorch model
pytorch_model = SimpleNN(X_train.shape[1])

# Create DataLoaders for PyTorch
train_loader, val_loader, test_loader = create_data_loaders(X_train, y_train, X_val, y_val, X_test, y_test)

# Train the PyTorch model
train_pytorch_model(pytorch_model, train_loader, val_loader, epochs=50)

# Evaluate the PyTorch model
evaluate_pytorch_model(pytorch_model, test_loader)

# Save the PyTorch model
save_pytorch_model(pytorch_model, 'pytorch_model.pth')

# Load the PyTorch model
pytorch_model_loaded = load_pytorch_model(SimpleNN, X_train.shape[1], 'pytorch_model.pth')

# Augment image data (if working with images)
# augmented_data = augment_image_data(X_train)

# Preprocess text data
# texts = ["This is a sample text.", "Another example sentence."]
# processed_texts_nltk = preprocess_text_nltk(texts)
# processed_texts_spacy = preprocess_text_spacy(texts)

from sklearn.model_selection import GridSearchCV from keras.wrappers.scikit_learn import KerasClassifier

Hyperparameter tuning for Keras models

def create_keras_model(optimizer='adam', init='glorot_uniform'): model = keras.Sequential([ keras.layers.Dense(64, input_shape=(X_train.shape[1],), kernel_initializer=init, activation='relu'), keras.layers.Dense(64, kernel_initializer=init, activation='relu'), keras.layers.Dense(1, kernel_initializer=init, activation='sigmoid') ]) model.compile(optimizer=optimizer, loss='binary_crossentropy', metrics=['accuracy']) return model

def tune_keras_model(X_train, y_train): model = KerasClassifier(build_fn=create_keras_model, verbose=0) param_grid = { 'batch_size': [10, 20, 40], 'epochs': [10, 50, 100], 'optimizer': ['SGD', 'Adam'], 'init': ['glorot_uniform', 'normal', 'uniform'] } grid = GridSearchCV(estimator=model, param_grid=param_grid, n_jobs=-1, cv=3) grid_result = grid.fit(X_train, y_train)

print(f"Best: {grid_result.best_score_} using {grid_result.best_params_}")
return grid_result.best_params_

Example of a more complex Keras model

def build_complex_keras_model(input_shape): model = keras.Sequential([ keras.layers.Dense(128, activation='relu', input_shape=input_shape), keras.layers.Dropout(0.5), keras.layers.Dense(64, activation='relu'), keras.layers.Dropout(0.5), keras.layers.Dense(32, activation='relu'), keras.layers.Dense(1, activation='sigmoid') ])

model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
return model

Function to handle categorical data

def handle_categorical_data(data, categorical_columns): for column in categorical_columns: data[column] = LabelEncoder().fit_transform(data[column]) return data

Example of preprocessing time series data

def preprocess_time_series(data, window_size): X, y = [], [] for i in range(len(data) - window_size): X.append(data[i:(i + window_size)]) y.append(data[i + window_size]) return np.array(X), np.array(y)

Example of a simple RNN model for time series prediction

def build_rnn_model(input_shape): model = keras.Sequential([ keras.layers.SimpleRNN(50, activation='relu', input_shape=input_shape), keras.layers.Dense(1) ])

model.compile(optimizer='adam', loss='mse')
return model

Example usage

if name == "main":

Assuming you have a CSV file with data

data = load_data('data.csv')
categorical_columns = ['category', 'sub_category']
data = handle_categorical_data(data, categorical_columns)
processed_data = preprocess_data(data)

# Split data into features and labels
X = processed_data.drop('label', axis=1).values
y = processed_data['label'].values

# Split data into training, validation, and test sets
X_train, X_temp, y_train, y_temp = train_test_split(X, y, test_size=0.3, random_state=42)
X_val, X_test, y_val, y_test = train_test_split(X_temp, y_temp, test_size=0.5, random_state=42)

# Hyperparameter tuning for Keras model
best_params = tune_keras_model(X_train, y_train)

# Build and train a more complex Keras model
complex_keras_model = build_complex_keras_model((X_train.shape[1],))
keras_callbacks = get_keras_callbacks('best_complex_keras_model.h5')
history = train_keras_model(complex_keras_model, X_train, y_train, X_val, y_val, epochs=50, batch_size=32, callbacks=keras_callbacks)

# Plot training history
plot_training_history(history)

# Evaluate the complex Keras model
evaluate_keras_model(complex_keras_model, X_test, y_test)

# Save the complex Keras model
save_keras_model(complex_keras_model, 'complex_keras_model.h5')

# Load the complex Keras model
complex_keras_model_loaded = load_keras_model('complex_keras_model.h5')

# Build and train a PyTorch model
pytorch_model = SimpleNN(X_train.shape[1])
train_loader, val_loader, test_loader = create_data_loaders(X_train, y_train, X_val, y_val, X_test, y_test)
train_pytorch_model(pytorch_model, train_loader, val_loader, epochs=50)

# Evaluate the PyTorch model
evaluate_pytorch_model(pytorch_model, test_loader)

# Save the PyTorch model
save_pytorch_model(pytorch_model, 'pytorch_model.pth')

# Load the PyTorch model
pytorch_model_loaded = load_pytorch_model(SimpleNN, X_train.shape[1], 'pytorch_model.pth')

# Augment image data (if working with images)
# augmented_data = augment_image_data(X_train)

# Preprocess text data
# texts = ["This is a sample text.", "Another example sentence."]
# processed_texts_nltk = preprocess_text_nltk(texts)
# processed_texts_spacy = preprocess_text_spacy(texts)

# Example for time series data
# Assuming data is a time series in a 1D numpy array
# window_size = 10
# X_ts, y_ts = preprocess_time_series(data, window_size)
# rnn_model = build_rnn_model((window_size, 1))
# rnn_model.fit(X_ts, y_ts, epochs=20, batch_size=32, validation_split=0.2)

from tensorflow.keras.applications import VGG16 from tensorflow.keras.models import Model from tensorflow.keras.layers import Dense, Flatten, Dropout from tensorflow.keras.optimizers import Adam import transformers from transformers import BertTokenizer, TFBertModel import shap

Transfer Learning with VGG16

def build_transfer_learning_model(input_shape, num_classes): base_model = VGG16(weights='imagenet', include_top=False, input_shape=input_shape)

# Freeze the base model
for layer in base_model.layers:
    layer.trainable = False

x = base_model.output
x = Flatten()(x)
x = Dense(1024, activation='relu')(x)
x = Dropout(0.5)(x)
predictions = Dense(num_classes, activation='softmax')(x)

model = Model(inputs=base_model.input, outputs=predictions)

model.compile(optimizer=Adam(lr=1e-4), loss='categorical_crossentropy', metrics=['accuracy'])
return model

Advanced NLP Preprocessing using BERT

def preprocess_text_bert(texts, max_length=128): tokenizer = BertTokenizer.from_pretrained('bert-base-uncased') encoded_inputs = tokenizer(texts, padding=True, truncation=True, max_length=max_length, return_tensors='tf') return encoded_inputs

Explainability using SHAP

def explain_model_predictions(model, X):

Assuming the model is a Keras model and X is the dataset

explainer = shap.KernelExplainer(model.predict, X)
shap_values = explainer.shap_values(X)
shap.summary_plot(shap_values, X)

Example usage

if name == "main":

Load and preprocess image data

# Assuming image_paths is a list of file paths and image_size is a tuple (height, width)
# image_paths = [...]
# image_size = (224, 224)
# images = load_image_data(image_paths, image_size)

# Transfer learning model for image classification
# num_classes = 10  # Example for 10 classes
# input_shape = (image_size[0], image_size[1], 3)
# transfer_model = build_transfer_learning_model(input_shape, num_classes)

# Assuming you have X_train, y_train, X_val, y_val for training
# history = transfer_model.fit(X_train, y_train, validation_data=(X_val, y_val), epochs=20, batch_size=32)

# Load and preprocess text data using BERT
# texts = ["This is a sample text.", "Another example sentence."]
# encoded_texts = preprocess_text_bert(texts)

# Assuming you have a BERT model for text classification
# bert_model = TFBertModel.from_pretrained('bert-base-uncased')

# Explain model predictions using SHAP
# Assuming you have a trained model and a dataset X
# explain_model_predictions(trained_model, X)

# Continue with other tasks as needed...

from imblearn.over_sampling import SMOTE from imblearn.under_sampling import RandomUnderSampler from imblearn.pipeline import Pipeline from tensorflow.keras.layers import LSTM, Embedding import autokeras as ak

Handling Imbalanced Datasets

def balance_data(X, y): smote = SMOTE(sampling_strategy='auto', random_state=42) under_sampler = RandomUnderSampler(sampling_strategy='auto', random_state=42)

pipeline = Pipeline([
    ('over', smote),
    ('under', under_sampler)
])

X_res, y_res = pipeline.fit_resample(X, y)
return X_res, y_res

More Complex Model Architecture with LSTM

def build_lstm_model(input_shape, vocab_size, embedding_dim): model = keras.Sequential([ Embedding(input_dim=vocab_size, output_dim=embedding_dim, input_length=input_shape[0]), LSTM(128, return_sequences=True), LSTM(64), Dense(1, activation='sigmoid') ])

model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
return model

Automated Machine Learning with AutoKeras

def build_autokeras_model(input_shape, num_classes): model = ak.ImageClassifier( overwrite=True, max_trials=3 ) model.fit(input_shape, num_classes, epochs=10) return model

Example usage

if name == "main":

Handling Imbalanced Datasets

data = load_data('data.csv')
X = data.drop('label', axis=1).values
y = data['label'].values

X_balanced, y_balanced = balance_data(X, y)

# Splitting the balanced data
X_train, X_temp, y_train, y_temp = train_test_split(X_balanced, y_balanced, test_size=0.3, random_state=42)
X_val, X_test, y_val, y_test = train_test_split(X_temp, y_temp, test_size=0.5, random_state=42)

# Building and training an LSTM model
vocab_size = 10000  # Example vocabulary size
embedding_dim = 100  # Example embedding dimension
input_shape = X_train.shape[1:]

lstm_model = build_lstm_model(input_shape, vocab_size, embedding_dim)
history = lstm_model.fit(X_train, y_train, validation_data=(X_val, y_val), epochs=20, batch_size=32)

# Plotting training history
plot_training_history(history)

# Evaluating the LSTM model
evaluate_keras_model(lstm_model, X_test, y_test)

# Saving the LSTM model
save_keras_model(lstm_model, 'lstm_model.h5')

# Loading the LSTM model
lstm_model_loaded = load_keras_model('lstm_model.h5')

# Automated Machine Learning with AutoKeras
input_shape = (224, 224, 3)  # Example image input shape
num_classes = 10  # Example number of classes
autokeras_model = build_autokeras_model(input_shape, num_classes)

# Assuming image_data and labels are preloaded
# autokeras_model.fit(image_data, labels, epochs=10)

# Continue with other tasks as needed...