GitHub Repository: probml/pyprobml
Path: blob/master/notebooks/book1/13/mlp_mnist_tf.ipynb
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Kernel: Python 3 (ipykernel)

MLP on (Fashion) MNIST using TF 2.0

In [1]:

try:
    # %tensorflow_version only exists in Colab.
    %tensorflow_version 2.x
    IS_COLAB = True
except Exception:
    IS_COLAB = False

# TensorFlow ≥2.0 is required
try:
    import tensorflow as tf
except ModuleNotFoundError:
    %pip install -qq tensorflow
    import tensorflow as tf
from tensorflow import keras

assert tf.__version__ >= "2.0"

if not tf.config.list_physical_devices("GPU"):
    print("No GPU was detected. DNNs can be very slow without a GPU.")
    if IS_COLAB:
        print("Go to Runtime > Change runtime and select a GPU hardware accelerator.")

Out[1]:

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    Found existing installation: tensorboard 2.9.0
    Uninstalling tensorboard-2.9.0:
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Note: you may need to restart the kernel to use updated packages.

2022-05-14 05:15:42.904069: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory
2022-05-14 05:15:42.904090: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.

No GPU was detected. DNNs can be very slow without a GPU.

2022-05-14 05:15:44.553337: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcuda.so.1'; dlerror: libcuda.so.1: cannot open shared object file: No such file or directory
2022-05-14 05:15:44.553363: W tensorflow/stream_executor/cuda/cuda_driver.cc:269] failed call to cuInit: UNKNOWN ERROR (303)
2022-05-14 05:15:44.553379: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:156] kernel driver does not appear to be running on this host (anand-HP-Laptop-14s-dq2xxx): /proc/driver/nvidia/version does not exist

In [2]:

# Standard Python libraries
from __future__ import absolute_import, division, print_function, unicode_literals
import os
import time
import numpy as np
import glob
import matplotlib.pyplot as plt
import PIL

try:
    import imageio
except ModuleNotFoundError:
    %pip install -qq imageio
    import imageio
from IPython import display
import sklearn
from time import time

np.random.seed(0)

try:
    from probml_utils.mnist_helper_tf import *
except ModuleNotFoundError:
    %pip install -qq git+https://github.com/probml/probml-utils.git
    from probml_utils.mnist_helper_tf import *

Out[2]:

Collecting git+https://github.com/probml/probml-utils.git
  Cloning https://github.com/probml/probml-utils.git to /tmp/pip-req-build-cc5y8gq7
  Running command git clone -q https://github.com/probml/probml-utils.git /tmp/pip-req-build-cc5y8gq7
  Resolved https://github.com/probml/probml-utils.git to commit 1cf7cf7b168d8f91e78e20f4464c60e4f693491b
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Building wheels for collected packages: probml-utils, jax, TexSoup
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Successfully built probml-utils jax TexSoup
Installing collected packages: grpcio, googleapis-common-protos, httplib2, grpcio-status, google-crc32c, google-api-core, uritemplate, proto-plus, jaxlib, jax, google-resumable-media, google-cloud-core, google-auth-httplib2, dm-tree, tensorflow-probability, google-cloud-storage, google-cloud-firestore, google-api-python-client, chex, cachecontrol, TexSoup, graphviz, firebase-admin, distrax, probml-utils
  Attempting uninstall: grpcio
    Found existing installation: grpcio 1.46.0
    Uninstalling grpcio-1.46.0:
      Successfully uninstalled grpcio-1.46.0
Successfully installed TexSoup-0.3.1 cachecontrol-0.12.11 chex-0.1.3 distrax-0.1.2 dm-tree-0.1.7 firebase-admin-5.2.0 google-api-core-2.7.3 google-api-python-client-2.47.0 google-auth-httplib2-0.1.0 google-cloud-core-2.3.0 google-cloud-firestore-2.4.0 google-cloud-storage-2.3.0 google-crc32c-1.3.0 google-resumable-media-2.3.2 googleapis-common-protos-1.56.1 graphviz-0.20 grpcio-1.46.1 grpcio-status-1.46.1 httplib2-0.20.4 jax-0.3.10 jaxlib-0.3.10 probml-utils-0.1.dev52+g1cf7cf7 proto-plus-1.20.3 tensorflow-probability-0.16.0 uritemplate-4.1.1
Note: you may need to restart the kernel to use updated packages.

In [3]:

train_images, train_labels, test_images, test_labels, class_names = get_dataset(FASHION=False)
print(train_images.shape)
plot_dataset(train_images, train_labels, class_names)

Out[3]:

(60000, 28, 28)

In [4]:

model = keras.Sequential(
    [
        keras.layers.Flatten(input_shape=(28, 28)),
        keras.layers.Dense(128, activation=tf.nn.relu),
        keras.layers.Dense(128, activation=tf.nn.relu),
        keras.layers.Dense(10, activation=tf.nn.softmax),
    ]
)

model.summary()

model.compile(optimizer="adam", loss="sparse_categorical_crossentropy", metrics=["accuracy"])


# We just train for 1 epochs because (1) it is faster, and
# (2) it produces more errors, which makes for a more interesting plot :)
time_start = time()
model.fit(train_images, train_labels, epochs=1)
print("time spent training {:0.3f}".format(time() - time_start))

Out[4]:

Model: "sequential"
_________________________________________________________________
 Layer (type)                Output Shape              Param #   
=================================================================
 flatten (Flatten)           (None, 784)               0         
                                                                 
 dense (Dense)               (None, 128)               100480    
                                                                 
 dense_1 (Dense)             (None, 128)               16512     
                                                                 
 dense_2 (Dense)             (None, 10)                1290      
                                                                 
=================================================================
Total params: 118,282
Trainable params: 118,282
Non-trainable params: 0
_________________________________________________________________

2022-05-14 05:16:43.431578: I tensorflow/core/platform/cpu_feature_guard.cc:151] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 AVX512F FMA
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
2022-05-14 05:16:43.653962: W tensorflow/core/framework/cpu_allocator_impl.cc:82] Allocation of 188160000 exceeds 10% of free system memory.

1875/1875 [==============================] - 2s 871us/step - loss: 0.2288 - accuracy: 0.9319
time spent training 2.223

In [5]:

# Overall accuracy
train_loss, train_acc = model.evaluate(train_images, train_labels)
print("Train accuracy:", train_acc)
test_loss, test_acc = model.evaluate(test_images, test_labels)
print("Test accuracy:", test_acc)

Out[5]:

  90/1875 [>.............................] - ETA: 1s - loss: 0.0953 - accuracy: 0.9743  

2022-05-14 05:16:45.832119: W tensorflow/core/framework/cpu_allocator_impl.cc:82] Allocation of 188160000 exceeds 10% of free system memory.

1875/1875 [==============================] - 1s 519us/step - loss: 0.1004 - accuracy: 0.9700
Train accuracy: 0.9699666500091553
129/313 [===========>..................] - ETA: 0s - loss: 0.1547 - accuracy: 0.9547

2022-05-14 05:16:46.995403: W tensorflow/core/framework/cpu_allocator_impl.cc:82] Allocation of 31360000 exceeds 10% of free system memory.

313/313 [==============================] - 0s 791us/step - loss: 0.1162 - accuracy: 0.9646
Test accuracy: 0.9646000266075134

In [6]:

# To apply prediction to a single image, we need to reshape to an (N,D,D) tensor
# where N=1
img = test_images[0]
img = np.expand_dims(img, 0)
print(img.shape)
predictions_single = model.predict(img)
print(predictions_single.shape)

Out[6]:

(1, 28, 28)
(1, 10)

In [7]:

predictions = model.predict(test_images)
print(np.shape(predictions))
ndx = find_interesting_test_images(predictions, test_labels)
plot_interesting_test_results(test_images, test_labels, predictions, class_names, ndx)

Out[7]:

(10000, 10)
(354,)

2022-05-14 05:16:47.452415: W tensorflow/core/framework/cpu_allocator_impl.cc:82] Allocation of 31360000 exceeds 10% of free system memory.

In [8]:

model_epoch = model  # save old model

# Train for 1 more epochs
time_start = time()
model.fit(train_images, train_labels, epochs=1)
print("time spent training {:0.3f}".format(time() - time_start))

Out[8]:

 182/1875 [=>............................] - ETA: 1s - loss: 0.0915 - accuracy: 0.9748

2022-05-14 05:16:48.643030: W tensorflow/core/framework/cpu_allocator_impl.cc:82] Allocation of 188160000 exceeds 10% of free system memory.

1875/1875 [==============================] - 2s 861us/step - loss: 0.0948 - accuracy: 0.9711
time spent training 1.875

In [9]:

# Overall accuracy
train_loss, train_acc = model.evaluate(train_images, train_labels)
print("Train accuracy:", train_acc)
test_loss, test_acc = model.evaluate(test_images, test_labels)
print("Test accuracy:", test_acc)

Out[9]:

1875/1875 [==============================] - 1s 530us/step - loss: 0.0622 - accuracy: 0.9810
Train accuracy: 0.9809666872024536
313/313 [==============================] - 0s 744us/step - loss: 0.0896 - accuracy: 0.9732
Test accuracy: 0.9732000231742859

In [11]:

predictions = model.predict(test_images)
print(np.shape(predictions))
# test_ndx = find_interesting_test_images(predictions) # re-use old inddices
plot_interesting_test_results(test_images, test_labels, predictions, class_names, ndx)

Out[11]:

(10000, 10)

MLP on (Fashion) MNIST using TF 2.0

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