The deep learning folder contains scripts implementing various deep learning models using TensorFlow and Keras.
Description: Feedforward neural network for binary classification on synthetic data.
Dependencies: tensorflow, numpy
Code:
import numpy as np
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense
np.random.seed(42)
X = np.random.rand(1000, 2)
y = (X[:, 0] + X[:, 1] > 1).astype(int)
model = Sequential([
Dense(16, activation='relu', input_dim=2),
Dense(8, activation='relu'),
Dense(1, activation='sigmoid')
])
model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
model.fit(X, y, epochs=50, batch_size=32, validation_split=0.2)
Description: Deep neural network for Iris dataset classification with dropout layers.
Dependencies: tensorflow, scikit-learn
Code:
from sklearn.datasets import load_iris
from sklearn.preprocessing import StandardScaler
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Dropout
from tensorflow.keras.utils import to_categorical
iris = load_iris()
X, y = iris.data, iris.target
y = to_categorical(y, num_classes=3)
scaler = StandardScaler()
X = scaler.fit_transform(X)
model = Sequential([
Dense(64, activation='relu', input_dim=4),
Dropout(0.2),
Dense(32, activation='relu'),
Dropout(0.2),
Dense(3, activation='softmax')
])
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
model.fit(X, y, epochs=100, batch_size=16, validation_split=0.2)
Description: Bidirectional LSTM model for IMDB sentiment analysis.
Dependencies: tensorflow
Code:
from tensorflow.keras.datasets import imdb
from tensorflow.keras.preprocessing.sequence import pad_sequences
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Embedding, Bidirectional, LSTM, Dense
(X_train, y_train), (X_test, y_test) = imdb.load_data(num_words=10000)
X_train = pad_sequences(X_train, maxlen=200)
X_test = pad_sequences(X_test, maxlen=200)
model = Sequential([
Embedding(10000, 128, input_length=200),
Bidirectional(LSTM(64)),
Dense(1, activation='sigmoid')
])
model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
model.fit(X_train, y_train, epochs=5, batch_size=32, validation_data=(X_test, y_test))
Description: CNN for digit recognition using the MNIST dataset.
Dependencies: tensorflow, matplotlib
Code:
import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense, Dropout
from tensorflow.keras.datasets import mnist
from tensorflow.keras.utils import to_categorical
(x_train, y_train), (x_test, y_test) = mnist.load_data()
x_train = x_train.reshape(-1, 28, 28, 1).astype('float32') / 255.0
x_test = x_test.reshape(-1, 28, 28, 1).astype('float32') / 255.0
y_train = to_categorical(y_train, num_classes=10)
y_test = to_categorical(y_test, num_classes=10)
model = Sequential([
Conv2D(32, (3, 3), activation='relu', input_shape=(28, 28, 1)),
MaxPooling2D((2, 2)),
Conv2D(64, (3, 3), activation='relu'),
MaxPooling2D((2, 2)),
Flatten(),
Dense(128, activation='relu'),
Dropout(0.5),
Dense(10, activation='softmax')
])
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
model.fit(x_train, y_train, epochs=10, batch_size=32, validation_data=(x_test, y_test))
Description: CNN for CIFAR-10 image classification.
Dependencies: tensorflow
Code:
from tensorflow.keras.datasets import cifar10
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense, Dropout
from tensorflow.keras.utils import to_categorical
(X_train, y_train), (X_test, y_test) = cifar10.load_data()
X_train = X_train.astype('float32') / 255.0
X_test = X_test.astype('float32') / 255.0
y_train = to_categorical(y_train, num_classes=10)
y_test = to_categorical(y_test, num_classes=10)
model = Sequential([
Conv2D(32, (3, 3), activation='relu', input_shape=(32, 32, 3)),
MaxPooling2D((2, 2)),
Conv2D(64, (3, 3), activation='relu'),
MaxPooling2D((2, 2)),
Flatten(),
Dense(128, activation='relu'),
Dropout(0.5),
Dense(10, activation='softmax')
])
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
model.fit(X_train, y_train, epochs=20, batch_size=64, validation_data=(X_test, y_test))
Description: RNN for sentiment analysis on a custom dataset.
Dependencies: tensorflow, numpy
Code:
import numpy as np
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Embedding, SimpleRNN, Dense
from tensorflow.keras.preprocessing.text import Tokenizer
from tensorflow.keras.preprocessing.sequence import pad_sequences
texts = ["great movie", "terrible film", "awesome story", "bad plot"]
labels = [1, 0, 1, 0]
tokenizer = Tokenizer(num_words=100)
tokenizer.fit_on_texts(texts)
sequences = tokenizer.texts_to_sequences(texts)
X = pad_sequences(sequences, maxlen=5)
y = np.array(labels)
model = Sequential([
Embedding(100, 32, input_length=5),
SimpleRNN(32),
Dense(1, activation='sigmoid')
])
model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
model.fit(X, y, epochs=10, batch_size=2)
Description: Transfer learning with VGG16 on Fashion MNIST dataset.
Dependencies: tensorflow
Code:
from tensorflow.keras.datasets import fashion_mnist
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Flatten
from tensorflow.keras.applications import VGG16
from tensorflow.keras.utils import to_categorical
(X_train, y_train), (X_test, y_test) = fashion_mnist.load_data()
X_train = np.stack([X_train]*3, axis=-1).astype('float32') / 255.0
X_test = np.stack([X_test]*3, axis=-1).astype('float32') / 255.0
y_train = to_categorical(y_train, num_classes=10)
y_test = to_categorical(y_test, num_classes=10)
base_model = VGG16(weights='imagenet', include_top=False, input_shape=(28, 28, 3))
model = Sequential([
base_model,
Flatten(),
Dense(128, activation='relu'),
Dense(10, activation='softmax')
])
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
model.fit(X_train, y_train, epochs=5, batch_size=32, validation_data=(X_test, y_test))
Description: LSTM for time series forecasting on stock data.
Dependencies: tensorflow, pandas, numpy
Code:
import pandas as pd
import numpy as np
from sklearn.preprocessing import MinMaxScaler
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import LSTM, Dense
data = pd.read_csv('stock_data.csv')
prices = data['Close'].values.reshape(-1, 1)
scaler = MinMaxScaler()
prices = scaler.fit_transform(prices)
def create_sequences(data, seq_length):
X, y = [], []
for i in range(len(data) - seq_length):
X.append(data[i:i + seq_length])
y.append(data[i + seq_length])
return np.array(X), np.array(y)
X, y = create_sequences(prices, seq_length=10)
model = Sequential([
LSTM(50, activation='relu', input_shape=(10, 1)),
Dense(1)
])
model.compile(optimizer='adam', loss='mse')
model.fit(X, y, epochs=50, batch_size=32, validation_split=0.2)