import aim
aim.init()
from aim import Profiler
Profiler.init()
import tensorflow as tf
# Import MNIST data
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("/tmp/data/", one_hot=True)
from examples.tf_profiler_model import neural_net
learning_rate = 0.1
num_steps = 500
batch_size = 128
display_step = 100
# Network Parameters
n_hidden_1 = 256 # 1st layer number of neurons
n_hidden_2 = 256 # 2nd layer number of neurons
num_input = 784 # MNIST data input (img shape: 28*28)
num_classes = 10 # MNIST total classes (0-9 digits)
# tf Graph input
X = tf.placeholder('float', [None, num_input])
Y = tf.placeholder('float', [None, num_classes])
# Store layers weight & bias
weights = {
from torchvision import models
from PIL import Image
import torch
import torchvision.transforms as T
import numpy as np
from aim import track
import aim
aim.init(True)
images = [
Image.open('./data/bird.jpg'),
Image.open('./data/car.jpeg'),
]
labels = {
0: 'background',
1: 'aeroplane',
2: 'bicycle',
3: 'bird',
4: 'boat',
5: 'bottle',
6: 'bus',
7: 'car',
8: 'cat',
9: 'chair',
10: 'cow',
11: 'diningtable',
12: 'dog',
13: 'horse',
import time
import tensorflow as tf
# Import MNIST data
from tensorflow.examples.tutorials.mnist import input_data
import aim
aim.init(overwrite=True)
from aim import Profiler
Profiler.init(auto_detect_cycles=False, aggregate=Profiler.MEAN)
mnist = input_data.read_data_sets('/tmp/data/', one_hot=True)
learning_rate = 0.1
num_steps = 500
batch_size = 128
display_step = 100
# Network Parameters
n_hidden_1 = 256
n_hidden_2 = 256
num_input = 784
num_classes = 10
# tf Graph input
X = tf.placeholder('float', [None, num_input])
Y = tf.placeholder('float', [None, num_classes])
# Store layers weight & bias
weights = {
import keras
from keras.datasets import mnist
from keras.models import Sequential
from keras.layers import Dense, Dropout, Flatten
from keras.layers import Conv2D, MaxPooling2D
from keras import backend as K
import aim
# Init aim with overwrite=False, every run will be committed and pushed
aim.init(overwrite=False)
from aim import Profiler
# initialize profiler
Profiler.init(sec_interval=2, squash=10)
batch_size = 128
num_classes = 10
epochs = 12
# input image dimensions
img_rows, img_cols = 28, 28
# the data, split between train and test sets
(x_train, y_train), (x_test, y_test) = mnist.load_data()
if K.image_data_format() == 'channels_first':
x_train = x_train.reshape(x_train.shape[0], 1, img_rows, img_cols)
x_test = x_test.reshape(x_test.shape[0], 1, img_rows, img_cols)
input_shape = (1, img_rows, img_cols)
else:
x_train = x_train.reshape(x_train.shape[0], img_rows, img_cols, 1)