himanshurawlani

def train_model(model):
    model.compile(optimizer='adam',
                  loss='sparse_categorical_crossentropy',
                  metrics=['accuracy'])

    # Creating Keras callbacks 
    tensorboard_callback = keras.callbacks.TensorBoard(log_dir=log_dir, histogram_freq=1)
    model_checkpoint_callback = keras.callbacks.ModelCheckpoint(
        'training_checkpoints/weights.{epoch:02d}-{val_loss:.2f}.hdf5', period=5)
    os.makedirs('training_checkpoints/', exist_ok=True)

himanshurawlani

from tensorflow.keras.preprocessing.image import ImageDataGenerator

datagen = ImageDataGenerator(rotation_range=20,
                             width_shift_range=0.2,
                             height_shift_range=0.2,
                             horizontal_flip=True)

# Performing model training for the given number of epochs
for e in range(epochs):
    print('Epoch', e)

himanshurawlani

from tensorflow import keras

# Create the base model from the pre-trained model MobileNet V2
base_model = keras.applications.InceptionV3(input_shape=IMG_SHAPE,
# We cannot use the top classification layer of the pre-trained model as it contains 1000 classes.
# It also restricts our input dimensions to that which this model is trained on (default: 299x299)
                                               include_top=False, 
                                               weights='imagenet')

himanshurawlani

def build_model():
    
    # Using Sequential API to stack up the layers
    model = keras.Sequential([
        base_model,
        keras.layers.GlobalAveragePooling2D(),
        keras.layers.Dense(metadata.features['label'].num_classes, 
                           activation='softmax')
    ])
    

himanshurawlani

# Un-freeze the top layers of the model
base_model.trainable = True
# Let's take a look to see how many layers are in the base model
print("Number of layers in the base model: ", len(base_model.layers))

# Fine tune from this layer onwards
fine_tune_at = 249

# Freeze all the layers before the `fine_tune_at` layer
for layer in base_model.layers[:fine_tune_at]:

himanshurawlani

from tensorflow import keras

# '/1' specifies the version of a model, or "servable" we want to export
path_to_saved_model = 'SavedModel/inceptionv3_128_tf_flowers/1'

# Saving the keras model in SavedModel format
keras.experimental.export_saved_model(inception_model, path_to_saved_model)

# Load the saved keras model back
restored_saved_model = keras.experimental.load_from_saved_model(path_to_saved_model)

himanshurawlani

import json, requests
from tensorflow.keras.preprocessing.image import img_to_array, load_img
import numpy as np

image_path = 'sunflower.jpg'
# Loading and pre-processing our input image
img = image.img_to_array(image.load_img(image_path, target_size=(128, 128))) / 255.
img = np.expand_dims(img, axis=0)
payload = {"instances": img.tolist()}

himanshurawlani

def augment_data(image, label):
  print("Augment data called!")
  image = tf.image.random_flip_left_right(image)
  image = tf.image.random_contrast(image, lower=0.0, upper=1.0)
  # Add more augmentation of your choice
  return image, label

train = train.map(augment_data)

himanshurawlani

import tensorflow as tf

def FCN_model(len_classes=5, dropout_rate=0.2):
    
    # Input layer
    input = tf.keras.layers.Input(shape=(None, None, 3))

    # A convolution block
    x = tf.keras.layers.Conv2D(filters=32, kernel_size=3, strides=1)(input)
    x = tf.keras.layers.Dropout(dropout_rate)(x)

himanshurawlani

# Uncomment the below line if you're using dense layers
# x = tf.keras.layers.GlobalMaxPooling2D()(x)

# Fully connected layer 1
# x = tf.keras.layers.Dropout(dropout_rate)(x)
# x = tf.keras.layers.BatchNormalization()(x)
# x = tf.keras.layers.Dense(units=64)(x)
# x = tf.keras.layers.Activation('relu')(x)

# Fully connected layer 1