BATCH_SIZE = 100
image_width = 227
image_height = 227
channels = 3
num_classes = 10
# Call the cnn/alexnet model
model = AlexNet((image_width, image_height, channels), num_classes)
# Model configuration
model.compile(optimizer=tf.keras.optimizers.Adam(0.001),
loss='categorical_crossentropy',
metrics=['acc'])
# Summary
model.summary()
# Directories for the datasets
train_dir = '/home/mike/Documents/image_gesture/dset_data/train'
val_dir = '/home/mike/Documents/image_gesture/dset_data/validation'
test_dir = '/home/mike/Documents/image_gesture/dset_data/test'
# Preprocess the images
train_datagen = ImageDataGenerator(rescale=1.0 / 255)
train_generator = train_datagen.flow_from_directory(train_dir,
target_size=(image_width,
image_height),
batch_size=BATCH_SIZE,
class_mode='categorical')
def run_experiment(n, large_data_set=False, generator=False):
"""
Run an experiment. One experiment loads the dataset, trains the model, and outputs the evaluation metrics after
training.
:param n: Number of experiments to perform
:param large_data_set: Set to True of you want to save the large dataset to hard disk and use generator for training
:param generator: Set to True is you want to use a generator to train the network.
:return: n/a
"""
for experiments in range(n):
if large_data_set:
save_data()
else:
data_train, data_test, data_val, info = load_data()
data_train, data_test, data_val,\
train_images, train_labels, test_images, test_labels,\
val_images, val_labels = preprocess_data(data_train, data_test, data_val)
visualize(data_train, data_test, info)
# Fit the model on the training data and validate on the validation data.
if generator:
train_images = np.load(
'/home/mike/Documents/Alexnet_Client_Backend/file_names.npy'
)
train_labels = np.load(
'/home/mike/Documents/Alexnet_Client_Backend/oh_labels.npy'
)
train_data = DataGenerator(train_images, train_labels,
batch_size)
else:
# Make the images, label paris into the tf.data.Dataset, shuffle the data and specify its batch size.
train_data = tf.data.Dataset.from_tensor_slices(
(train_images, train_labels))
train_data = train_data.repeat().shuffle(6149).batch(100)
test_data = tf.data.Dataset.from_tensor_slices(
(test_images, test_labels))
test_data = test_data.repeat().shuffle(1020).batch(batch_size)
val_data = tf.data.Dataset.from_tensor_slices(
(val_images, val_labels))
val_data = val_data.batch(batch_size)
# With the three parameters, the client script calls the AlexNet model(as a class) in alexnet.py
model = AlexNet(train_data, test_data, val_data)
# Compile the model using Adam optimizer and categorical_crossentropy loss function.
model.compile(optimizer='adam',
loss='categorical_crossentropy',
metrics=['acc', top_5_acc])
# Give the model structure summary after complete the above-mentioned calling.
model.summary()
if generator:
file_names = np.load(data_path + 'file_names.npy')
num_files = file_names.shape[0]
del file_names
model.fit_generator(generator=train_data,
steps_per_epoch=int(num_files //
batch_size),
epochs=epochs,
verbose=verbose,
validation_data=val_data)
else:
model.fit(train_data,
epochs=epochs,
validation_data=val_data,
verbose=verbose,
steps_per_epoch=steps_per_epoch)
predictions(model, test_images, test_labels, num_examples=5)
# Evaluate the model
loss, accuracy, top_5 = model.evaluate(test_data,
verbose=verbose,
steps=5)
# Append the metrics to the scores lists in case you are performing an experiment which involves comparing
# training over many iterations.
loss_scores.append(loss)
acc_scores.append(accuracy)
top_5_acc_scores.append(top_5)
# Print the mean, std, min, and max of the validation accuracy scores from your experiment.
print(acc_scores)
print('Mean_accuracy={}'.format(np.mean(acc_scores)),
'STD_accuracy={}'.format(np.std(acc_scores)))
print('Min_accuracy={}'.format(np.min(acc_scores)),
'Max_accuracy={}'.format(np.max(acc_scores)))
