def main(session_name, epochs, batch_size, optimizer, loss, metrics):
# kafka_dataset = tfio.kafka.KafkaDataset(
# topics='deeplearnint_training_1', servers='localhost', group='', eof=False, timeout=1000,
# config_global=None, config_topic=None, message_key=False
# )
fashion_mnist = keras.datasets.fashion_mnist
(train_images, train_labels), (test_images,
test_labels) = fashion_mnist.load_data()
model = keras.Sequential([
keras.layers.Flatten(input_shape=(28, 28)),
keras.layers.Dense(128, activation='relu'),
keras.layers.Dense(10)
])
model.compile(optimizer=optimizer, loss=loss, metrics=metrics)
model.fit(train_images,
train_labels,
epochs=epochs,
batch_size=batch_size,
callbacks=[KafkaCallback(session_name)])
test_loss, test_acc = model.evaluate(
test_images,
test_labels,
verbose=2,
callbacks=[KafkaCallback(session_name)])
print('\nTest accuracy:', test_acc)
def main(session_name):
fashion_mnist = keras.datasets.fashion_mnist
(train_images, train_labels), (test_images,
test_labels) = fashion_mnist.load_data()
model = keras.Sequential([
keras.layers.Flatten(input_shape=(28, 28)),
keras.layers.Dense(128, activation='relu'),
keras.layers.Dense(10)
])
model.compile(
optimizer='adam',
loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
metrics=[
'accuracy',
tf.keras.metrics.AUC(),
])
model.fit(train_images,
train_labels,
epochs=10,
batch_size=64,
callbacks=[KafkaCallback(session_name)])
test_loss, test_acc = model.evaluate(
test_images,
test_labels,
verbose=2,
callbacks=[KafkaCallback(session_name)])
print('\nTest accuracy:', test_acc)
def main(session_name, epochs, batch_size, optimizer, loss, metrics):
fashion_mnist = keras.datasets.fashion_mnist
(train_images, train_labels), (test_images,
test_labels) = fashion_mnist.load_data()
model = keras.Sequential([
keras.layers.Flatten(input_shape=(28, 28)),
keras.layers.Dense(128, activation='relu'),
keras.layers.Dense(2)
])
model.compile(optimizer=optimizer, loss=loss, metrics=metrics)
model.fit(train_images,
train_labels,
epochs=epochs,
batch_size=batch_size,
callbacks=[KafkaCallback(session_name)])
test_loss, test_acc = model.evaluate(
test_images,
test_labels,
verbose=2,
callbacks=[KafkaCallback(session_name)])
print('\nTest accuracy:', test_acc)
def main(session_name, epochs, batch_size, optimizer, loss, metrics):
# kafka_dataset = tfio.kafka.KafkaDataset(
# topics='deeplearnint_training_1', servers='localhost', group='', eof=False, timeout=1000,
# config_global=None, config_topic=None, message_key=False
# )
_URL = 'https://storage.googleapis.com/mledu-datasets/cats_and_dogs_filtered.zip'
path_to_zip = tf.keras.utils.get_file('cats_and_dogs.zip',
origin=_URL,
extract=True)
PATH = os.path.join(os.path.dirname(path_to_zip), 'cats_and_dogs_filtered')
train_dir = os.path.join(PATH, 'train')
validation_dir = os.path.join(PATH, 'validation')
train_cats_dir = os.path.join(
train_dir, 'cats') # directory with our training cat pictures
train_dogs_dir = os.path.join(
train_dir, 'dogs') # directory with our training dog pictures
validation_cats_dir = os.path.join(
validation_dir, 'cats') # directory with our validation cat pictures
validation_dogs_dir = os.path.join(
validation_dir, 'dogs') # directory with our validation dog pictures
num_cats_tr = len(os.listdir(train_cats_dir))
num_dogs_tr = len(os.listdir(train_dogs_dir))
num_cats_val = len(os.listdir(validation_cats_dir))
num_dogs_val = len(os.listdir(validation_dogs_dir))
total_train = num_cats_tr + num_dogs_tr
total_val = num_cats_val + num_dogs_val
print('total training cat images:', num_cats_tr)
print('total training dog images:', num_dogs_tr)
print('total validation cat images:', num_cats_val)
print('total validation dog images:', num_dogs_val)
print("--")
print("Total training images:", total_train)
print("Total validation images:", total_val)
# batch_size = 128
# epochs = 15
IMG_HEIGHT = 150
IMG_WIDTH = 150
train_image_generator = ImageDataGenerator(
rescale=1. / 255) # Generator for our training data
validation_image_generator = ImageDataGenerator(
rescale=1. / 255) # Generator for our validation data
train_data_gen = train_image_generator.flow_from_directory(
batch_size=batch_size,
directory=train_dir,
shuffle=True,
target_size=(IMG_HEIGHT, IMG_WIDTH),
class_mode='binary')
val_data_gen = validation_image_generator.flow_from_directory(
batch_size=batch_size,
directory=validation_dir,
target_size=(IMG_HEIGHT, IMG_WIDTH),
class_mode='binary')
sample_training_images, _ = next(train_data_gen)
model = Sequential([
Conv2D(16,
3,
padding='same',
activation='relu',
input_shape=(IMG_HEIGHT, IMG_WIDTH, 3)),
MaxPooling2D(),
Conv2D(32, 3, padding='same', activation='relu'),
MaxPooling2D(),
Conv2D(64, 3, padding='same', activation='relu'),
MaxPooling2D(),
Flatten(),
Dense(512, activation='relu'),
Dense(1, activation='sigmoid')
])
# model.compile(optimizer=optimizer,
# loss=loss,
# metrics=metrics)
model.compile(optimizer=optimizer, loss=loss, metrics=metrics)
model.summary()
# model.fit(train_images, train_labels, epochs=epochs, batch_size=batch_size, callbacks=[KafkaCallback(session_name)])
# test_loss, test_acc = model.evaluate(test_images, test_labels, verbose=2, callbacks=[KafkaCallback(session_name)])
history = model.fit(train_data_gen,
steps_per_epoch=total_train // batch_size,
epochs=epochs,
validation_data=val_data_gen,
validation_steps=total_val // batch_size,
callbacks=[KafkaCallback(session_name)])