mirror of
https://github.com/clearml/clearml
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131 lines
4.4 KiB
Python
131 lines
4.4 KiB
Python
from __future__ import absolute_import, division, print_function, unicode_literals
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import tensorflow as tf
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from tensorflow.keras.layers import Dense, Flatten, Conv2D
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from tensorflow.keras import Model
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from trains import Task
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task = Task.init(project_name='examples',
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task_name='Tensorflow v2 mnist with summaries')
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# Load and prepare the MNIST dataset.
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mnist = tf.keras.datasets.mnist
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(x_train, y_train), (x_test, y_test) = mnist.load_data()
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x_train, x_test = x_train / 255.0, x_test / 255.0
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# Add a channels dimension
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x_train = x_train[..., tf.newaxis]
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x_test = x_test[..., tf.newaxis]
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# Use tf.data to batch and shuffle the dataset
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train_ds = tf.data.Dataset.from_tensor_slices((x_train, y_train)).shuffle(10000).batch(32)
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test_ds = tf.data.Dataset.from_tensor_slices((x_test, y_test)).batch(32)
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# Build the tf.keras model using the Keras model subclassing API
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class MyModel(Model):
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def __init__(self):
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super(MyModel, self).__init__()
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self.conv1 = Conv2D(32, 3, activation='relu')
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self.flatten = Flatten()
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self.d1 = Dense(128, activation='relu')
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self.d2 = Dense(10, activation='softmax')
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def call(self, x):
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x = self.conv1(x)
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x = self.flatten(x)
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x = self.d1(x)
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return self.d2(x)
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# Create an instance of the model
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model = MyModel()
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# Choose an optimizer and loss function for training
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loss_object = tf.keras.losses.SparseCategoricalCrossentropy()
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optimizer = tf.keras.optimizers.Adam()
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# Select metrics to measure the loss and the accuracy of the model.
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# These metrics accumulate the values over epochs and then print the overall result.
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train_loss = tf.keras.metrics.Mean(name='train_loss', dtype=tf.float32)
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train_accuracy = tf.keras.metrics.SparseCategoricalAccuracy(name='train_accuracy')
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test_loss = tf.keras.metrics.Mean(name='test_loss', dtype=tf.float32)
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test_accuracy = tf.keras.metrics.SparseCategoricalAccuracy(name='test_accuracy')
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# Use tf.GradientTape to train the model
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@tf.function
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def train_step(images, labels):
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with tf.GradientTape() as tape:
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predictions = model(images)
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loss = loss_object(labels, predictions)
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gradients = tape.gradient(loss, model.trainable_variables)
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optimizer.apply_gradients(zip(gradients, model.trainable_variables))
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train_loss(loss)
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train_accuracy(labels, predictions)
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# Test the model
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@tf.function
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def test_step(images, labels):
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predictions = model(images)
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t_loss = loss_object(labels, predictions)
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test_loss(t_loss)
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test_accuracy(labels, predictions)
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# Set up summary writers to write the summaries to disk in a different logs directory
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train_log_dir = '/tmp/logs/gradient_tape/train'
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test_log_dir = '/tmp/logs/gradient_tape/test'
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train_summary_writer = tf.summary.create_file_writer(train_log_dir)
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test_summary_writer = tf.summary.create_file_writer(test_log_dir)
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# Set up checkpoints manager
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ckpt = tf.train.Checkpoint(step=tf.Variable(1), optimizer=optimizer, net=model)
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manager = tf.train.CheckpointManager(ckpt, '/tmp/tf_ckpts', max_to_keep=3)
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ckpt.restore(manager.latest_checkpoint)
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if manager.latest_checkpoint:
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print("Restored from {}".format(manager.latest_checkpoint))
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else:
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print("Initializing from scratch.")
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# Start training
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EPOCHS = 5
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for epoch in range(EPOCHS):
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for images, labels in train_ds:
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train_step(images, labels)
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with train_summary_writer.as_default():
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tf.summary.scalar('loss', train_loss.result(), step=epoch)
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tf.summary.scalar('accuracy', train_accuracy.result(), step=epoch)
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ckpt.step.assign_add(1)
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if int(ckpt.step) % 1 == 0:
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save_path = manager.save()
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print("Saved checkpoint for step {}: {}".format(int(ckpt.step), save_path))
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for test_images, test_labels in test_ds:
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test_step(test_images, test_labels)
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with test_summary_writer.as_default():
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tf.summary.scalar('loss', test_loss.result(), step=epoch)
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tf.summary.scalar('accuracy', test_accuracy.result(), step=epoch)
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template = 'Epoch {}, Loss: {}, Accuracy: {}, Test Loss: {}, Test Accuracy: {}'
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print(template.format(epoch+1,
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train_loss.result(),
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train_accuracy.result()*100,
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test_loss.result(),
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test_accuracy.result()*100))
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# Reset the metrics for the next epoch
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train_loss.reset_states()
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train_accuracy.reset_states()
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test_loss.reset_states()
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test_accuracy.reset_states()
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