import matplotlib as mpl
import matplotlib.pyplot as plt
%matplotlib inline
import numpy as np
import sklearn
import pandas as pd
import os
import sys
import time
import tensorflow as tf
from tensorflow import keras
print(tf.__version__)print(sys.version_info)for module in mpl, np ,pd, sklearn, tf, keras:print(module.__name__, module.__version__)
WARNING:tensorflow:From <ipython-input-5-856460fb2370>:16: DatasetV1.make_one_shot_iterator (from tensorflow.python.data.ops.dataset_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use `for ... in dataset:` to iterate over a dataset. If using `tf.estimator`, return the `Dataset` object directly from your input function. As a last resort, you can use `tf.compat.v1.data.make_one_shot_iterator(dataset)`.
(20, 784)
(20,)
WARNING:tensorflow:From <ipython-input-6-b426c99b6704>:14: dense (from tensorflow.python.layers.core) is deprecated and will be removed in a future version.
Instructions for updating:
Use keras.layers.Dense instead.
WARNING:tensorflow:From E:\Anaconda\anaconda\envs\tensorflow1\lib\site-packages\tensorflow_core\python\layers\core.py:187: Layer.apply (from tensorflow.python.keras.engine.base_layer) is deprecated and will be removed in a future version.
Instructions for updating:
Please use `layer.__call__` method instead.
WARNING:tensorflow:From E:\Anaconda\anaconda\envs\tensorflow1\lib\site-packages\tensorflow_core\python\ops\losses\losses_impl.py:121: where (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.where in 2.0, which has the same broadcast rule as np.where