Snippet code for basic CNN implementation using tensorflow

cnn_basic_example.py

import tensorflow as tf
import tensorflow_docs as tfdocs

cnn_model = tf.keras.Sequential([
    tf.keras.layers.Conv2D(32, (3, 3), activation='relu', input_shape=train_imgs.shape[1: 4]), 
    tf.keras.layers.MaxPooling2D(2,2),
    tf.keras.layers.Conv2D(32, (3, 3), activation='relu'),
    tf.keras.layers.MaxPooling2D(2,2),
    tf.keras.layers.Conv2D(32, (3, 3), activation='relu'),
    tf.keras.layers.MaxPooling2D(2,2),
    tf.keras.layers.Flatten(),
    tf.keras.layers.Dense(128, activation=tf.nn.relu),
    tf.keras.layers.Dense(N_CLASS, activation=tf.nn.softmax)
])

cnn_model.compile(optimizer=tf.keras.optimizers.Adam(),
                  loss=tf.losses.SparseCategoricalCrossentropy(),
                  metrics=['accuracy'])

early_stopping = tf.keras.callbacks.EarlyStopping(monitor='val_loss', patience=10, mode='min')
cnn_hist = cnn_model.fit(
    train_imgs, train_labels, epochs=500, validation_split=0.2, verbose=0,
    batch_size=64, callbacks=[tfdocs.modeling.EpochDots(), early_stopping]
)

# plot the performance during each epochs
plt.figure(figsize=(11, 7))
plotter = tfdocs.plots.HistoryPlotter()
plotter.plot({'Basic': cnn_hist}, metric='accuracy')
plt.show()