staceysv

from keras.callbacks import Callback
import numpy as np
import pandas
from sklearn.metrics import precision_score
import time
import wandb

# other metrics from sklearn we may use
# confusion_matrix, f1_score, recall_score

staceysv

from keras.preprocessing import image
from keras.models import Model
from keras.layers import Dense, GlobalAveragePooling2D
from keras.preprocessing.image import ImageDataGenerator
from keras import backend as K

import wandb
from wandb.keras import WandbCallback

from keras_callbacks import PerClassMetrics

staceysv

import argparse
import time
import os

import tensorflow
from tensorflow.keras import optimizers
from tensorflow.keras.preprocessing.image import ImageDataGenerator

from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Conv2D, MaxPooling2D

staceysv

#!/usr/bin/env python
import csv
from PIL import Image
import pickle
import os

img_size = 84

test_csv_file = "../../Code/few-shot-ssl-public/fewshot/data/mini_imagenet_split/Ravi/test.csv"
train_csv_file = "../../Code/few-shot-ssl-public/fewshot/data/mini_imagenet_split/Ravi/train.csv"

staceysv

{
  "$schema": "https://vega.github.io/schema/vega/v5.json",
  "padding": 5,
  "signals": [
    {
      "name": "binOffset",
      "value": 0
    },
    {
      "name": "binStep",

staceysv

# mol_sol_train.py
#--------------
# Load molecular solubility dataset, featurize, train FC net regression model,
# and evaluate by R^2 score.

import deepchem as dc
from deepchem.utils.evaluate import Evaluator
from deepchem.utils.save import load_from_disk
import numpy as np
import numpy.random

staceysv

name: dnn cling test
project: deepchem_molsol
description: go deeper and wider on FC net
program: ml_train.py
method: bayes
metric:
  name: r2
  goal: maximize
parameters:
  batch_size:

staceysv

#! /usr/bin/env python
  
import deepchem as dc
from deepchem.utils import download_url
from deepchem.utils.evaluate import Evaluator
import pandas as pd

import nglview
import tempfile
import os