Create README.md

Author: Harshraj1301

README.md

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+
+# Generating Text Using LSTM
+
+This repository contains code and resources for generating text using Long Short-Term Memory (LSTM) neural networks. The project demonstrates how to build and train an LSTM model for text generation, using a sample dataset.
+
+## Repository Structure
+
+```
+Generating-Text-Using-LSTM/
+│
+├── .gitattributes
+├── Harshraj_Jadeja_HW3_LSTM_TEXT_GEN.ipynb
+└── README.md
+```
+
+- `.gitattributes`: Configuration file to ensure consistent handling of files across different operating systems.
+- `Harshraj_Jadeja_HW3_LSTM_TEXT_GEN.ipynb`: Jupyter Notebook containing the code for building and training the LSTM model, as well as the text generation process.
+- `README.md`: This file. Provides an overview of the project and instructions for getting started.
+
+## Getting Started
+
+To get started with this project, follow the steps below:
+
+### Prerequisites
+
+Make sure you have the following installed:
+
+- Python 3.x
+- Jupyter Notebook
+- Required Python libraries (listed in `requirements.txt`)
+
+### Installation
+
+1. Clone this repository to your local machine:
+
+```bash
+git clone https://github.com/Harshraj1301/Generating-Text-Using-LSTM.git
+```
+
+2. Navigate to the project directory:
+
+```bash
+cd Generating-Text-Using-LSTM
+```
+
+3. Install the required Python libraries:
+
+```bash
+pip install -r requirements.txt
+```
+
+### Usage
+
+1. Open the Jupyter Notebook:
+
+```bash
+jupyter notebook Harshraj_Jadeja_HW3_LSTM_TEXT_GEN.ipynb
+```
+
+2. Follow the instructions in the notebook to run the code cells and generate text using the LSTM model.
+
+### Code Explanation
+
+The notebook `Harshraj_Jadeja_HW3_LSTM_TEXT_GEN.ipynb` includes the following steps:
+
+1. **Data Preprocessing**: Loading and preprocessing the text data to make it suitable for training the LSTM model.
+2. **Model Building**: Constructing the LSTM model using Keras.
+3. **Model Training**: Training the LSTM model on the preprocessed text data.
+4. **Text Generation**: Using the trained model to generate new text sequences.
+
+Here are the contents of the notebook:
+
+# Harshraj Jadeja
+
+# Long Short-term Memory for Text Generation
+
+This notebook uses LSTM neural network to generate text from Nietzsche's writings.
+
+## Dataset
+
+### Get the data
+Nietzsche's writing dataset is available online. The following code download the dataset.
+
+### Visualize data
+
+### Clean data
+
+We cut the text in sequences of maxlen characters with a jump size of 3.
+The features for each example is a matrix of size maxlen*num of chars.
+The label for each example is a vector of size num of chars, which represents the next character.
+
+## The model
+
+### Build the model - fill in this box
+
+we need a recurrent layer with input shape (maxlen, len(chars)) and a dense layer with output size  len(chars)
+
+### Inspect the model
+
+Use the `.summary` method to print a simple description of the model
+
+### Train the model
+
+## Code Cells
+
+```python
+import matplotlib.pyplot as plt
+import numpy as np
+import pandas as pd
+import time
+import random
+import sys
+import io
+import tensorflow as tf
+from tensorflow import keras
+from tensorflow.keras import layers
+from tensorflow.keras import optimizers
+from tensorflow.keras.callbacks import LambdaCallback
+from tensorflow.keras.utils import get_file
+```
+
+```python
+path = get_file(
+    'nietzsche.txt',
+    origin='https://s3.amazonaws.com/text-datasets/nietzsche.txt')
+with io.open(path, encoding='utf-8') as f:
+    text = f.read().lower()
+```
+
+```python
+print('corpus length:', len(text))
+```
+
+```python
+print(text[10:513])
+```
+
+```python
+chars = sorted(list(set(text)))
+# total nomber of characters
+print('total chars:', len(chars))
+```
+
+```python
+# create (character, index) and (index, character) dictionary
+char_indices = dict((c, i) for i, c in enumerate(chars))
+indices_char = dict((i, c) for i, c in enumerate(chars))
+```
+
+```python
+# cut the text in semi-redundant sequences of maxlen characters
+maxlen = 40
+step = 3
+sentences = []
+next_chars = []
+for i in range(0, len(text) - maxlen, step):
+    sentences.append(text[i: i + maxlen])
+    next_chars.append(text[i + maxlen])
+print('nb sequences:', len(sentences))
+```
+
+```python
+print('Vectorization...')
+x = np.zeros((len(sentences), maxlen, len(chars)), dtype=np.bool_)
+y = np.zeros((len(sentences), len(chars)), dtype=np.bool_)
+for i, sentence in enumerate(sentences):
+    for t, char in enumerate(sentence):
+        x[i, t, char_indices[char]] = 1
+    y[i, char_indices[next_chars[i]]] = 1
+```
+
+```python
+# Define the number of units in the LSTM layer.
+# This is a hyperparameter that represents the dimensionality of the output space.
+# More units can allow the model to capture more complex patterns but also increases computational complexity.
+lstm_units = 128  # Adjust this number based on the complexity of the task and computational constraints.
+
+# Initialize the Sequential model
+model = tf.keras.Sequential([
+    # Add an LSTM layer as the first layer of the model
+    # input_shape is required as the LSTM layer's first layer to let it know the shape of the input it should expect
+    # Here, input_shape=(maxlen, len(chars)) means each input sequence will be of length 'maxlen'
+    # and each character in the sequence is represented as a one-hot encoded vector of length 'len(chars)'
+    tf.keras.layers.LSTM(lstm_units, input_shape=(maxlen, len(chars))),
+    
+    # Add a Dense output layer
+    # The number of units equals the number of unique characters (len(chars))
+    # This is because we want to output a probability distribution over all possible characters
+    # Softmax activation function is used to output probabilities
+    tf.keras.layers.Dense(len(chars), activation='softmax'),
+])
+
+# Compile the model
+# 'categorical_crossentropy' is used as the loss function since this is a multi-class classification problem
+# 'adam' optimizer is chosen for efficient stochastic gradient descent optimization
+# Accuracy is monitored as a metric to observe the performance of the model during training
+model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
+
+# Display the model's architecture
+model.summary()
+```
+
+```python
+model.summary()
+```
+
+```python
+def sample(preds, temperature=1.0):
+    # helper function to sample an index from a probability array
+    preds = np.asarray(preds).astype('float64')
+    preds = np.log(preds) / temperature
+    exp_preds = np.exp(preds)
+    preds = exp_preds / np.sum(exp_preds)
+    probas = np.random.multinomial(1, preds, 1)
+    return np.argmax(probas)
+```
+
+```python
+class PrintLoss(keras.callbacks.Callback):
+    def on_epoch_end(self, epoch, _):
+        # Function invoked at end of each epoch. Prints generated text.
+        print()
+        print('----- Generating text after Epoch: %d' % epoch)
+
+        start_index = random.randint(0, len(text) - maxlen - 1)
+        for diversity in [0.5, 1.0]:
+            print('----- diversity:', diversity)
+
+            generated = ''
+            sentence = text[start_index: start_index + maxlen]
+            generated += sentence
+            print('----- Generating with seed: "' + sentence + '"')
+            sys.stdout.write(generated)
+
+            for i in range(400):
+                x_pred = np.zeros((1, maxlen, len(chars)))
+                for t, char in enumerate(sentence):
+                    x_pred[0, t, char_indices[char]] = 1.
+
+                preds = model.predict(x_pred, verbose=0)[0]
+                next_index = sample(preds, diversity)
+                next_char = indices_char[next_index]
+
+                sentence = sentence[1:] + next_char
+
+                sys.stdout.write(next_char)
+                sys.stdout.flush()
+            print()
+```
+
+```python
+EPOCHS = 60
+BATCH = 128
+
+early_stop = keras.callbacks.EarlyStopping(monitor='val_loss', patience=2)
+
+history = model.fit(x, y,
+                    batch_size = BATCH,
+                    epochs = EPOCHS,
+                    validation_split = 0.2,
+                    verbose = 1,
+                    callbacks = [early_stop, PrintLoss()])
+```
+
+## Results
+
+The notebook includes the results of the text generation process, showcasing how the trained LSTM model generates sequences of text based on the input data.
+
+## Contributing
+
+If you'd like to contribute to this project, please follow these steps:
+
+1. Fork the repository.
+2. Create a new branch: `git checkout -b feature-branch-name`
+3. Make your changes and commit them: `git commit -m 'Add some feature'`
+4. Push to the branch: `git push origin feature-branch-name`
+5. Submit a pull request.
+
+## License
+
+This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
+
+## Acknowledgements
+
+- This project was created as part of an assignment by Harshraj Jadeja.
+- Thanks to the open-source community for providing valuable resources and libraries for machine learning.
+
+---
+
+Feel free to modify this `README.md` file as per your specific requirements and project details.