Image classification with keras and tensorflow in python|machine learning using python keras

In this tutorial we will discuss about Image classification with keras and tensorflow in python|machine learning using python keras.This example shows how to do image classification from scratch, starting from JPEG image files on disk, without leveraging pre-trained weights or a pre-made Keras Application model. We demonstrate the workflow on the Kaggle Cats vs Dogs binary classification dataset.We use the image_dataset_from_directory utility to generate the datasets, and we use Keras image preprocessing layers for image standardization and data augmentation.

step:-1 Import following libraries:

 import tensorflow as tf 
 from tensorflow import keras 
 from tensorflow.keras import layers 

step-2: download and extract data:

 !curl -O https://download.microsoft.com/download/3/E/1/3E1C3F21-ECDB-4869-8368-6DEBA77B919F/kagglecatsanddogs_3367a.zip

 !unzip -q kagglecatsanddogs_3367a.zip 

!ls

step-3:Get dataset

 image_size=(180, 180) 

 batch_size=32 

 train_ds=tf.keras.preprocessing.image_dataset_from_directory( 

  "PetImages", 

  validation_split=0.2, 

  subset="training", 

  seed=1337, 

  image_size=image_size, 

  batch_size=batch_size, 

)

 val_ds=tf.keras.preprocessing.image_dataset_from_directory( 

  "PetImages", 

  validation_split=0.2, 

  subset="validation", 

  seed=1337, 

  image_size=image_size, 

  batch_size=batch_size, 

)

step-4:visualize the dataset using matplotlib

 import matplotlib.pyplot as plt 

 plt.figure(figsize=(10, 10)) 
 for images, labels in train_ds.take(1): 
  for i in range(9): 
  ax=plt.subplot(3, 3, i + 1) 
  plt.imshow(images[i].numpy().astype("uint8")) 
  plt.title(int(labels[i])) 
  plt.axis("off") 

step-5:perform image augmentation

 data_augmentation=keras.Sequential( 

[

  layers.RandomFlip("horizontal"), 

  layers.RandomRotation(0.1), 

]

)

step-6:visualize the augmented data

 plt.figure(figsize=(10, 10)) 
 for images, _ in train_ds.take(1): 
  for i in range(9): 
  augmented_images=data_augmentation(images) 
  ax=plt.subplot(3, 3, i + 1) 
  plt.imshow(augmented_images[0].numpy().astype("uint8")) 
  plt.axis("off") 

step-7:dataset performance

 train_ds=train_ds.prefetch(buffer_size=32) 

 val_ds=val_ds.prefetch(buffer_size=32) 

step-8:make mechine model

 def make_model(input_shape, num_classes): 

  inputs=keras.Input(shape=input_shape) 

  x=data_augmentation(inputs) 

  # Entry block 

  x=layers.Rescaling(1.0 / 255)(x) 

  x=layers.Conv2D(32, 3, strides=2, padding="same")(x) 

  x=layers.BatchNormalization()(x) 

  x=layers.Activation("relu")(x) 

  x=layers.Conv2D(64, 3, padding="same")(x) 

  x=layers.BatchNormalization()(x) 

  x=layers.Activation("relu")(x) 

  previous_block_activation=x  

  for size in [128, 256, 512, 728]: 

  x=layers.Activation("relu")(x) 

  x=layers.SeparableConv2D(size, 3, padding="same")(x) 

  x=layers.BatchNormalization()(x) 

  x=layers.Activation("relu")(x) 

  x=layers.SeparableConv2D(size, 3, padding="same")(x) 

  x=layers.BatchNormalization()(x) 

  x=layers.MaxPooling2D(3, strides=2, padding="same")(x) 

  residual=layers.Conv2D(size, 1, strides=2, padding="same")( 

  previous_block_activation 

)

  x=layers.add([x, residual])  

  previous_block_activation=x  

  x=layers.SeparableConv2D(1024, 3, padding="same")(x) 

  x=layers.BatchNormalization()(x) 

  x=layers.Activation("relu")(x) 

  x=layers.GlobalAveragePooling2D()(x) 

  if num_classes==2: 

  activation="sigmoid" 

  units=1 

  else: 

  activation="softmax" 

  units=num_classes 

  x=layers.Dropout(0.5)(x) 

  outputs=layers.Dense(units, activation=activation)(x) 

  return keras.Model(inputs, outputs) 

 model=make_model(input_shape=image_size + (3,), num_classes=2) 

 keras.utils.plot_model(model, show_shapes=True) 

step-9:train a model

 epochs=50 

callbacks=[
  keras.callbacks.ModelCheckpoint("save_at_{epoch}.h5"), 
]
 model.compile( 
  optimizer=keras.optimizers.Adam(1e-3), 
  loss="binary_crossentropy", 
  metrics=["accuracy"], 
)
model.fit(
  train_ds, epochs=epochs, callbacks=callbacks, validation_data=val_ds, 
)

step-10:Execute inference on new dataset:

 img=keras.preprocessing.image.load_img( 

  "PetImages/Cat/6779.jpg", target_size=image_size 

)

 img_array=keras.preprocessing.image.img_to_array(img) 

 img_array=tf.expand_dims(img_array, 0)  

 predictions=model.predict(img_array) 

 score=predictions[0] 

 print( 

  "This image is %.2f percent cat and %.2f percent dog." 

  % (100 * (1 - score), 100 * score) 

)

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Image classification- Machine learning in python keras and tensorflow