def __init__(self, debug_x=None): super(KerasCallback, self).__init__() self.context = deepkit.context() self.context.debugger_controller.snapshot.subscribe(self.snapshot) self.debug_x = debug_x self.epoch = 0 self.data_validation = None self.data_validation_size = None self.current = {} self.last_batch_time = time.time() self.start_time = time.time() self.accuracy_metric = None self.all_losses = None self.loss_metric = None self.learning_rate_metric = None self.learning_rate_start = 0 self.last_debug_sent = 0
import asyncio import deepkit context = deepkit.context('deepkit.yml') acc = deepkit.create_metric('acc') async def bla(): for i in range(1000): await asyncio.sleep(1) context.epoch(i, 10) acc.send(i, i * 2) print("asdasd") asyncio.get_event_loop().run_until_complete(bla())
import random import deepkit context = deepkit.context() context.add_file(__file__) test = deepkit.create_metric('test') for i in range(100): deepkit.set_info(i, random.random()) total = 1_000_000; for i in range(1_000_000): test.send(i, random.random()) deepkit.epoch(i, total) print("Bye.")
import datetime import tensorflow as tf from tensorflow.keras import Model, layers, optimizers, datasets import deepkit from deepkit import ContextOptions context = deepkit.context(ContextOptions( # account='peter', # project='deepkitorg/tf2-keras-mnist' )) context.add_file('model.py') (x, y), (x_val, y_val) = datasets.fashion_mnist.load_data() x = x.reshape(x.shape[0], 28, 28, 1) x_val = x_val.reshape(x_val.shape[0], 28, 28, 1) x = x / 255.0 y = tf.one_hot(y, depth=10, dtype=tf.float32) y_val = tf.one_hot(y_val, depth=10) print('x/y shape:', x.shape, y.shape) def train_gen(): global x, y for x2, y2 in zip(x, y): yield (x2, x2), y2 # yield x2, y2 train_dataset = tf.data.Dataset.from_generator( train_gen,