main.py

#!/usr/bin/env python3
# -*- coding: utf-8 -*-
#
# Copyright 2023 James James Johnson. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================

"""
@author: James J Johnson
@url: https://www.linkedin.com/in/james-james-johnson/
"""

# Install this package if running on your local machine

# pip install protobuf==3.20.3

# pip install -q tensorflow-datasets

# pip install protobuf==4.23.3

# https://stackoverflow.com/questions/72485953/protobuf-incompatibility-when-installing-tensorflow-model-garden
# Google introduced a new breaking change in protobuf-4.21.0. Anything later than that will not work.
# You need to install protobuf 3.20.x or earlier (like version 3.20.3.)

# cudnn64_8.dll is missing for Windows
# https://docs.nvidia.com/deeplearning/cudnn/install-guide/index.html#install-windows
# https://stackoverflow.com/questions/66083545/could-not-load-dynamic-library-cudnn64-8-dll-dlerror-cudnn64-8-dll-not-found
# https://stackoverflow.com/questions/70210805/cudnn64-8-dll-not-found-but-i-have-it-installed-properly
# https://developer.nvidia.com/rdp/cudnn-download
# https://forums.developer.nvidia.com/t/missing-cudnn64-8-dll-file/198920
# https://docs.nvidia.com/deeplearning/sdk/cudnn-install/index.html
# https://docs.nvidia.com/deeplearning/sdk/

###############################################################################
# NVIDIA cuDNN:
#
# Installation:
#
# Support Matrix: Platform and software version compatibility
#     https://docs.nvidia.com/deeplearning/sdk/cudnn-support-matrix/index.html?ncid=em-prod-337416
# Installation Guide: Step-by-step instructions for installation and upgrade
#     https://docs.nvidia.com/deeplearning/sdk/cudnn-install/index.html?ncid=em-prod-337416
#
# Documentation:
#
# Developer Guide: Overview of programming model, features and formats supported
#     https://docs.nvidia.com/deeplearning/sdk/cudnn-developer-guide/index.html?ncid=em-prod-337416
# cuDNN API: Reference for cuDNN datatypes and APIs
#     https://docs.nvidia.com/deeplearning/sdk/cudnn-api/index.html?ncid=em-prod-337416
#
# Community:
#
# Developer Forum: Browse introductory “how-to” questions or discuss advanced tips-and-tricks for your application with the community
#     https://forums.developer.nvidia.com/c/ai-deep-learning/libraries-sdk/cudnn/90?ncid=em-prod-337416
# Bug Reporting: Help improve cuDNN by reporting bugs and filing enhancement requests for the cuDNN team
#     https://developer.nvidia.com/nvidia_bug/add?ncid=em-prod-337416
###############################################################################

###############################################################################
# Load packages

import tensorflow_datasets as tfds
import tensorflow as tf
import matplotlib.pyplot as plt

print(tfds.__version__) # 4.9.2
print(tf.__version__) # 2.9.1
# protobuf 3.20.3

###############################################################################
# Load data

dataset, info = tfds.load(
    'imdb_reviews/subwords8k',
    with_info=True,
    as_supervised=True)
tokenizer = info.features['text'].encoder

###############################################################################
# Define hyperparameters

buffer_size = 10000
batch_size = 32 # 256 run out of memory
embedding_dim = 64
lstm1_dim = 64
lstm2_dim = 32
dense_dim = 64
num_epochs = 10

###############################################################################
# Shuffle and pad data in datasets

train_data, validation_data = dataset['train'], dataset['test'], 
train_dataset = train_data.shuffle(buffer_size)
train_dataset = train_dataset.padded_batch(batch_size)
validation_dataset = validation_data.padded_batch(batch_size)

###############################################################################
# Define and compile model

if False :
    model = tf.keras.Sequential([
        tf.keras.layers.Embedding(tokenizer.vocab_size, embedding_dim),
        tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(lstm1_dim)),
        tf.keras.layers.Dense(dense_dim, activation='relu'),
        tf.keras.layers.Dense(1, activation='sigmoid')
    ])

if True:
    model = tf.keras.Sequential([
        tf.keras.layers.Embedding(tokenizer.vocab_size, embedding_dim),
        tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(
            lstm1_dim, return_sequences=True)),
        tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(
            lstm2_dim)),
        tf.keras.layers.Dense(dense_dim, activation='relu'),
        tf.keras.layers.Dense(1, activation='sigmoid')
    ])

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

###############################################################################
# Fit model

history = model.fit(
    train_dataset,
    epochs=num_epochs,
    validation_data=validation_dataset,
    )

###############################################################################
# Plot model fitting history

def plot_history(history, metric_name):
    plt.plot(history.history[metric_name])
    plt.plot(history.history['val_' + metric_name])
    plt.xlabel("Epoch")
    plt.ylabel(metric_name)
    plt.legend([metric_name, 'val_' + metric_name])
    plt.show()

plot_history(history, "loss")
plot_history(history, "accuracy")
plt.savefig("fitting_history.pdf", format="pdf", bbox_inches="tight")