tf2 keras image classifier

keras_image_classifier.py

#https://towardsdatascience.com/easy-image-classification-with-tensorflow-2-0-f734fee52d13
import tensorflow as tf
from tensorflow import keras
import datetime as dt
import pandas as pd
import sklearn.model_selection as sk
# tf.enable_eager_execution()
print(tf.__version__)

train_csv = pd.read_csv('data/train.csv')
# Prepend image filenames in train/ with relative path
filenames = ['data/train/' + fname for fname in train_csv['id'].tolist()]
labels = train_csv['has_cactus'].tolist()
train_filenames, val_filenames, train_labels, val_labels = sk.train_test_split(filenames,
                                                                            labels,
                                                                            train_size=0.9, 
                                                                            random_state=42)
                                                                            
train_data = tf.data.Dataset.from_tensor_slices(
  (tf.constant(train_filenames), tf.constant(train_labels))
)
val_data = tf.data.Dataset.from_tensor_slices(
  (tf.constant(val_filenames), tf.constant(val_labels))
)

IMAGE_SIZE = 96 # Minimum image size for use with MobileNetV2
BATCH_SIZE = 32
# Function to load and preprocess each image
def _parse_fn(filename, label):
    img = tf.io.read_file(filename)
    img = tf.image.decode_jpeg(img)
    img = (tf.cast(img, tf.float32)/127.5) - 1
    img = tf.image.resize(img, (IMAGE_SIZE, IMAGE_SIZE))
    return img, label

# Run _parse_fn over each example in train and val datasets
# Also shuffle and create batches
train_data = (train_data.map(_parse_fn).shuffle(buffer_size=10000).batch(BATCH_SIZE))
val_data = (val_data.map(_parse_fn).shuffle(buffer_size=10000).batch(BATCH_SIZE))

IMG_SHAPE = (IMAGE_SIZE, IMAGE_SIZE, 3)
# Pre-trained model with MobileNetV2
base_model = tf.keras.applications.MobileNetV2(
    input_shape=IMG_SHAPE,
    include_top=False,
    weights='imagenet'
)
# Freeze the pre-trained model weights
base_model.trainable = False
# Trainable classification head
maxpool_layer = tf.keras.layers.GlobalMaxPooling2D()
prediction_layer = tf.keras.layers.Dense(1, activation='sigmoid')
# Layer classification head with feature detector
model = tf.keras.Sequential([
    base_model,
    maxpool_layer,
    prediction_layer
])
learning_rate = 0.0001
# Compile the model
model.compile(optimizer=tf.keras.optimizers.Adam(lr=learning_rate), 
              loss='binary_crossentropy',
              metrics=['accuracy']
)

num_epochs = 30
steps_per_epoch = round(BATCH_SIZE)//BATCH_SIZE
val_steps = 20

#https://machinelearningmastery.com/display-deep-learning-model-training-history-in-keras/
history = model.fit(train_data.repeat(),
          epochs=num_epochs,
          steps_per_epoch = steps_per_epoch,
          validation_data=val_data.repeat(), 
          validation_steps=val_steps)