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computer_vision_iris_flower_dataset.py
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computer_vision_iris_flower_dataset.py
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# -*- coding: utf-8 -*-
"""Computer Vision - Iris Flower dataset.ipynb
Automatically generated by Colaboratory.
Original file is located at
https://colab.research.google.com/drive/1nVUy4ULwySjBmw_ZWIKYVos1nEmACOpU
**Developers' Guide:** [Amazon Machine Learning](https://docs.aws.amazon.com/pdfs/machine-learning/latest/dg/machinelearning-dg.pdf#cross-validation)
# **Iris Flower Classification**
The data set contains 3 classes of 50 instances each, where each class refers to a type of iris plant
### **Dataset available on:** [kaggle](https://www.kaggle.com/datasets/jeffheaton/iris-computer-vision)
#### **Importing the libraries**
"""
import tensorflow as tf
# image processing submodule ImageDataGenerator
from keras.preprocessing.image import ImageDataGenerator
# check the version of tensorflow if it's upated and compatible
tf.__version__
"""#### **Load the data**"""
!unzip -q Iris_flower.zip -d /content
"""#### **Data preprocessing**:
Preprocessing the entire dataset
"""
# creation of tranformation of the training dataset object 'train_datagen'
train_datagen = ImageDataGenerator(
# feature scaling
rescale=1./255,
# image augementation to prevent overfitting
shear_range=0.2,
zoom_range=0.2,
horizontal_flip=True)
# connect train_datagen object and import training dataset/images
training_set = train_datagen.flow_from_directory(
'/content/Iris_flower/training',
# resize and create batches to make the computation less intensive
target_size=(64, 64), # 64x64 for faster computation
batch_size=32, # 32 images in each batch
# set class mode to categorical since it's multi-class
class_mode='categorical')
"""#### **Model:**"""
# create a cnn model
def cnn_model(epoch, training_set):
"""Create the model"""
model = tf.keras.models.Sequential() # instantiate the cnn model
# add first convolution layer
model.add(tf.keras.layers.Conv2D(filters=32, kernel_size=3, activation='relu', input_shape=[64, 64, 3]))
# add first pooling layer
model.add(tf.keras.layers.MaxPooling2D(pool_size=2, strides=2))
# add second convolution layer
model.add(tf.keras.layers.Conv2D(filters=32, kernel_size=3, activation='relu'))
# add second pooling layer
model.add(tf.keras.layers.MaxPooling2D(pool_size=2, strides=2))
# add third convolutional layer
model.add(tf.keras.layers.Conv2D(filters=32, kernel_size=3, activation='relu'))
# add third pooling layer
model.add(tf.keras.layers.MaxPooling2D(pool_size=2, strides=2))
# add fourth convolutional layer
model.add(tf.keras.layers.Conv2D(filters=32, kernel_size=3, activation='relu'))
# add fourth pooling layer
model.add(tf.keras.layers.MaxPooling2D(pool_size=2, strides=2))
# create and flatten into one dimension vector
model.add(tf.keras.layers.Flatten())
# first fully connnected layer
model.add(tf.keras.layers.Dense(units=128, activation='relu'))
# OUTPUT LAYER
model.add(tf.keras.layers.Dense(units=3, activation='softmax'))
# An insight about the model
print("The model's Architecture:")
model.summary()
print("\n\n")
print("Training: note the data has been train and test on itself so watchout for overfitting")
# compile the model
model.compile(optimizer='adam', loss='categorical_crossentropy',metrics=['accuracy'])
"""Epoch callbacks and model fit"""
epochs = epoch
callbacks = [tf.keras.callbacks.TensorBoard(log_dir="logs",write_graph=True, write_images=False, update_freq="epoch",)]
# fit the model
model_cnn = model.fit(training_set,epochs = epochs, callbacks=callbacks)
return model_cnn
# Commented out IPython magic to ensure Python compatibility.
# %%time
# # call the model function and assign to the variable model
# model = cnn_model(30, training_set)
"""**Note:** the model hasn't perform well even though it's trained and validate on the same data, this can be because there's not enough data for the model to learn all the features and the parameters are too much. You can play with the architecture yourself by reducing and add more layers.
#### **Evaluation:**
"""
# import pandas and convert the dictionary to dataframe
import pandas as pd
Metrics = pd.DataFrame.from_dict(model.history, orient='index').T
Metrics
# let us see our loss
Metrics[["loss"]].plot();
# let us see our accuracy
Metrics[["accuracy"]].plot();
# Commented out IPython magic to ensure Python compatibility.
# Load the TensorBoard notebook extension
# %load_ext tensorboard
# %tensorboard --logdir logs
"""### **Now over to you download the notebook:**
- Split the data into train and test set
- play with parameters and Tune them
- Pickle and save the model
- write a function that can predict unseen iris flower
- for more please check my repository [ML vs DL](https://github.com/Kmohamedalie/CIFAR10_ML-vs-DL/blob/master/Colab_TPU_layers_Convolutional_neural_network.ipynb).
### **Sample code to pickle and save a model:**
<code>
<b>Save the model to disk:</b> <br>
import pickle
<!--# save the model to disk-->
filename = 'finalized_model.sav' <br>
pickle.dump(model, open(filename, 'wb')) <br>
<!--# some time later...-->
<!-- # load the model from disk -->
<b>Load the model from disk</b><br>
loaded_model = pickle.load(open(filename, 'rb')) <br>
result = loaded_model.score(X_test, Y_test) <br>
print(result)
</code>
## **Good Luck on your learning!!**
"""