The fruits dataset used in this model training is from Kaggle : fruits dataset
Importing library and modeules
import numpy as np
import tensorflow as tf
Train and Test data
learn more about pathlib
import pathlib
train_dir = pathlib.Path("../input/fruits/fruits-360_dataset/fruits-360/Training")
test_dir = pathlib.Path("../input/fruits/fruits-360_dataset/fruits-360/Test")
New function/Concept 'glog()'
The glob module is a useful part of the Python standard library. glob (short for global) is used to return all file paths that match a specific pattern.
We can use glob to search for a specific file pattern, or perhaps more usefully, search for files where the filename matches a certain pattern by using wildcard characters.
Learn more about glob
https://towardsdatascience.com/the-python-glob-module-47d82f4cbd2d
# Total number of images in training data-set
image_count = len(list(train_dir.glob('*/*.jpg')))
image_count
67692
Showing / Visualize Image
here we are using matplotlib for visualizing our data
we have open our image and converted to digits using Pillow
import matplotlib.pyplot as plt
import PIL
fruits = list(train_dir.glob('Banana/*.jpg'))
plt.figure(figsize=(10, 10))
for i in range(3):
plt.subplot(3, 3, i + 1)
img = PIL.Image.open(str(fruits[i]))
plt.imshow(img)
plt.axis('off')
plt.show()
Setting Up variables
batch_size = 32
img_height = 100
img_width = 100
Collecting Data
Used keras 'image_dataset_from_directory' API for collrcting data from directories
Learn more about 'image_dataset_from_directory'
https://www.tensorflow.org/api_docs/python/tf/keras/utils/image_dataset_from_directory
train_ds = tf.keras.preprocessing.image_dataset_from_directory(
train_dir,
validation_split=0.2,
subset='training',
seed=42,
image_size=(img_height, img_width),
batch_size=batch_size
)
Found 67692 files belonging to 131 classes. Using 54154 files for training.
val_ds = tf.keras.preprocessing.image_dataset_from_directory(
train_dir,
validation_split=0.2,
subset='validation',
seed=42,
image_size=(img_height, img_width),
batch_size=batch_size
)
Found 67692 files belonging to 131 classes. Using 13538 files for validation.
Visualizing friuts by classes
class_names = train_ds.class_names
num_classes = len(class_names)
plt.figure(figsize=(10, 10))
for images, labels in train_ds.take(1):
for i in range(25):
plt.subplot(5, 5, i + 1)
plt.imshow(images[i].numpy().astype('uint8'))
plt.title(class_names[labels[i]])
plt.axis('off')
Preprocessing/Setting Up Base Model
prefetch the data for faster training while model is trained
Learning more about prefetch and AUTOTUNE
AUTOTUNE : https://stackoverflow.com/questions/56613155/tensorflow-tf-data-autotune
AUTOTUNE = tf.data.experimental.AUTOTUNE
train_ds = train_ds.cache().shuffle(1000).prefetch(buffer_size=AUTOTUNE)
val_ds = val_ds.cache().prefetch(buffer_size=AUTOTUNE)
Data Augumentation
- Data augmentation is a set of techniques to artificially increase the amount of data by generating new data points from existing data. This includes making small changes to data or using deep learning models to generate new data points.
data_augmentation = tf.keras.Sequential([
tf.keras.layers.experimental.preprocessing.RandomFlip('horizontal'),
tf.keras.layers.experimental.preprocessing.RandomRotation(0.2)
])
Using ResNEt Model for Transfer Learning
Learn more about ResNet by going through paper
preprocess_input = tf.keras.applications.resnet.preprocess_input
base_model = tf.keras.applications.resnet.ResNet50(
input_shape=(img_height, img_width, 3),
include_top=False,
weights='imagenet'
)
Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/resnet/resnet50_weights_tf_dim_ordering_tf_kernels_notop.h5 94773248/94765736 [==============================] - 0s 0us/step 94781440/94765736 [==============================] - 0s 0us/step
- setting base model trainable to False so model take less time
base_model.trainable = False
global_average_layer = tf.keras.layers.GlobalAveragePooling2D()
prediction_layer = tf.keras.layers.Dense(num_classes)
Building Model
inputs = tf.keras.Input(shape=(100, 100, 3))
x = data_augmentation(inputs)
x = preprocess_input(x)
x = base_model(x, training=False)
x = global_average_layer(x)
x = tf.keras.layers.Dropout(0.2)(x)
outputs = prediction_layer(x)
model = tf.keras.Model(inputs=inputs, outputs=outputs)
optimizer = tf.keras.optimizers.Adam(learning_rate=0.0001)
model.compile(
optimizer=optimizer,
loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
metrics=['accuracy']
)
model.summary()
Model: "model" _________________________________________________________________ Layer (type) Output Shape Param #
\================================================================= input_2 (InputLayer) [(None, 100, 100, 3)] 0
_________________________________________________________________ sequential (Sequential) (None, 100, 100, 3) 0
_________________________________________________________________ tf.operators.getitem (Sl (None, 100, 100, 3) 0
_________________________________________________________________ tf.nn.bias_add (TFOpLambda) (None, 100, 100, 3) 0
_________________________________________________________________ resnet50 (Functional) (None, 4, 4, 2048) 23587712
_________________________________________________________________ global_average_pooling2d (Gl (None, 2048) 0
_________________________________________________________________ dropout (Dropout) (None, 2048) 0
_________________________________________________________________ dense (Dense) (None, 131) 268419
\================================================================= Total params: 23,856,131 Trainable params: 268,419 Non-trainable params: 23,587,712 _________________________________________________________________
Training the model
model.evaluate(val_ds)
2022-11-21 23:17:58.112151: I tensorflow/stream_executor/cuda/cuda_dnn.cc:369] Loaded cuDNN version 8005
424/424 [==============================] - 40s 73ms/step - loss: 6.1083 - accuracy: 0.0146
[6.10833215713501, 0.014551632106304169]
- evolution accuracy is very bad but wait for traning
epochs = 15
history = model.fit(
train_ds,
epochs=epochs,
validation_data=val_ds
)
Epoch 1/15
2022-11-21 23:18:54.026118: I tensorflow/core/kernels/data/shuffle_dataset_op.cc:175] Filling up shuffle buffer (this may take a while): 143 of 1000 2022-11-21 23:19:04.017250: I tensorflow/core/kernels/data/shuffle_dataset_op.cc:175] Filling up shuffle buffer (this may take a while): 288 of 1000 2022-11-21 23:19:14.002639: I tensorflow/core/kernels/data/shuffle_dataset_op.cc:175] Filling up shuffle buffer (this may take a while): 431 of 1000 2022-11-21 23:19:24.049675: I tensorflow/core/kernels/data/shuffle_dataset_op.cc:175] Filling up shuffle buffer (this may take a while): 579 of 1000 2022-11-21 23:19:34.018446: I tensorflow/core/kernels/data/shuffle_dataset_op.cc:175] Filling up shuffle buffer (this may take a while): 720 of 1000 2022-11-21 23:19:44.006225: I tensorflow/core/kernels/data/shuffle_dataset_op.cc:175] Filling up shuffle buffer (this may take a while): 865 of 1000
3/1693 [..............................] - ETA: 1:41 - loss: 6.8432 - accuracy: 0.0208
2022-11-21 23:19:53.219212: I tensorflow/core/kernels/data/shuffle_dataset_op.cc:228] Shuffle buffer filled.
1693/1693 [==============================] - 160s 51ms/step - loss: 1.3016 - accuracy: 0.7131 - val_loss: 0.2886 - val_accuracy: 0.9617 Epoch 2/15 1693/1693 [==============================] - 54s 32ms/step - loss: 0.2196 - accuracy: 0.9602 - val_loss: 0.1175 - val_accuracy: 0.9866 Epoch 3/15 1693/1693 [==============================] - 52s 31ms/step - loss: 0.1076 - accuracy: 0.9821 - val_loss: 0.0637 - val_accuracy: 0.9947 Epoch 4/15 1693/1693 [==============================] - 52s 31ms/step - loss: 0.0632 - accuracy: 0.9905 - val_loss: 0.0405 - val_accuracy: 0.9968 Epoch 5/15 1693/1693 [==============================] - 52s 31ms/step - loss: 0.0416 - accuracy: 0.9941 - val_loss: 0.0277 - val_accuracy: 0.9983 Epoch 6/15 1693/1693 [==============================] - 52s 31ms/step - loss: 0.0307 - accuracy: 0.9956 - val_loss: 0.0214 - val_accuracy: 0.9988 Epoch 7/15 1693/1693 [==============================] - 52s 31ms/step - loss: 0.0224 - accuracy: 0.9972 - val_loss: 0.0155 - val_accuracy: 0.9990 Epoch 8/15 1693/1693 [==============================] - 52s 31ms/step - loss: 0.0180 - accuracy: 0.9975 - val_loss: 0.0125 - val_accuracy: 0.9993 Epoch 9/15 1693/1693 [==============================] - 53s 31ms/step - loss: 0.0143 - accuracy: 0.9982 - val_loss: 0.0116 - val_accuracy: 0.9990 Epoch 10/15 1693/1693 [==============================] - 52s 31ms/step - loss: 0.0119 - accuracy: 0.9985 - val_loss: 0.0107 - val_accuracy: 0.9993 Epoch 11/15 1693/1693 [==============================] - 53s 31ms/step - loss: 0.0102 - accuracy: 0.9985 - val_loss: 0.0089 - val_accuracy: 0.9990 Epoch 12/15 1693/1693 [==============================] - 52s 31ms/step - loss: 0.0087 - accuracy: 0.9989 - val_loss: 0.0093 - val_accuracy: 0.9994 Epoch 13/15 1693/1693 [==============================] - 52s 31ms/step - loss: 0.0079 - accuracy: 0.9988 - val_loss: 0.0077 - val_accuracy: 0.9993 Epoch 14/15 1693/1693 [==============================] - 52s 31ms/step - loss: 0.0066 - accuracy: 0.9991 - val_loss: 0.0072 - val_accuracy: 0.9996 Epoch 15/15 1693/1693 [==============================] - 52s 31ms/step - loss: 0.0061 - accuracy: 0.9992 - val_loss: 0.0060 - val_accuracy: 0.9999
Visualization of Accuracy and loss
Loss
train_loss = history.history['loss']
val_loss = history.history['val_loss']
epochs_range = range(epochs)
plt.figure(figsize=(12, 10))
plt.plot(epochs_range, train_loss, label="Training Loss")
plt.plot(epochs_range, val_loss, label="Validation Loss")
plt.legend(loc='upper left')
plt.title('Training and Validation Loss')
plt.show()
Accuracy
train_acc = history.history['accuracy']
val_acc = history.history['val_accuracy']
epochs_range = range(epochs)
plt.figure(figsize=(12, 10))
plt.plot(epochs_range, train_acc, label="Training Accuracy")
plt.plot(epochs_range, val_acc, label="Validation Accuracy")
plt.legend(loc='upper left')
plt.title('Training and Validation Accuracy')
plt.show()
