MNIST-MLP Classification on Google Colab
MNIST-MLP Classification on Google Colab
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In this lesson we discuss in how to create a simple IPython Notebook to solve an image classification problem with Multi Layer Perceptron.
Pre-requisites
Install the following Python packages
- cloudmesh-installer
- cloudmesh-common
pip3 install cloudmesh-installer
pip3 install cloudmesh-common
Sample MLP with Tensorflow Keras
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import time
import numpy as np
from keras.models import Sequential
from keras.layers import Dense, Activation, Dropout
from keras.utils import to_categorical, plot_model
from keras.datasets import mnist
#import pydotplus
from keras.utils.vis_utils import model_to_dot
#from keras.utils.vis_utils import pydot
from cloudmesh.common.StopWatch import StopWatch
StopWatch.start("data-load")
(x_train, y_train), (x_test, y_test) = mnist.load_data()
StopWatch.stop("data-load")
num_labels = len(np.unique(y_train))
y_train = to_categorical(y_train)
y_test = to_categorical(y_test)
image_size = x_train.shape[1]
input_size = image_size * image_size
x_train = np.reshape(x_train, [-1, input_size])
x_train = x_train.astype('float32') / 255
x_test = np.reshape(x_test, [-1, input_size])
x_test = x_test.astype('float32') / 255
batch_size = 128
hidden_units = 512
dropout = 0.45
model = Sequential()
model.add(Dense(hidden_units, input_dim=input_size))
model.add(Activation('relu'))
model.add(Dropout(dropout))
model.add(Dense(hidden_units))
model.add(Activation('relu'))
model.add(Dropout(dropout))
model.add(Dense(hidden_units))
model.add(Activation('relu'))
model.add(Dropout(dropout))
model.add(Dense(hidden_units))
model.add(Activation('relu'))
model.add(Dropout(dropout))
model.add(Dense(num_labels))
model.add(Activation('softmax'))
model.summary()
plot_model(model, to_file='mlp-mnist.png', show_shapes=True)
StopWatch.start("compile")
model.compile(loss='categorical_crossentropy',
optimizer='adam',
metrics=['accuracy'])
StopWatch.stop("compile")
StopWatch.start("train")
model.fit(x_train, y_train, epochs=5, batch_size=batch_size)
StopWatch.stop("train")
StopWatch.start("test")
loss, acc = model.evaluate(x_test, y_test, batch_size=batch_size)
print("\nTest accuracy: %.1f%%" % (100.0 * acc))
StopWatch.stop("test")
StopWatch.benchmark()
Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz
11493376/11490434 [==============================] - 0s 0us/step
Model: "sequential"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
dense (Dense) (None, 512) 401920
_________________________________________________________________
activation (Activation) (None, 512) 0
_________________________________________________________________
dropout (Dropout) (None, 512) 0
_________________________________________________________________
dense_1 (Dense) (None, 512) 262656
_________________________________________________________________
activation_1 (Activation) (None, 512) 0
_________________________________________________________________
dropout_1 (Dropout) (None, 512) 0
_________________________________________________________________
dense_2 (Dense) (None, 512) 262656
_________________________________________________________________
activation_2 (Activation) (None, 512) 0
_________________________________________________________________
dropout_2 (Dropout) (None, 512) 0
_________________________________________________________________
dense_3 (Dense) (None, 512) 262656
_________________________________________________________________
activation_3 (Activation) (None, 512) 0
_________________________________________________________________
dropout_3 (Dropout) (None, 512) 0
_________________________________________________________________
dense_4 (Dense) (None, 10) 5130
_________________________________________________________________
activation_4 (Activation) (None, 10) 0
=================================================================
Total params: 1,195,018
Trainable params: 1,195,018
Non-trainable params: 0
_________________________________________________________________
Epoch 1/5
469/469 [==============================] - 14s 29ms/step - loss: 0.7886 - accuracy: 0.7334
Epoch 2/5
469/469 [==============================] - 14s 29ms/step - loss: 0.1981 - accuracy: 0.9433
Epoch 3/5
469/469 [==============================] - 14s 29ms/step - loss: 0.1546 - accuracy: 0.9572
Epoch 4/5
469/469 [==============================] - 14s 29ms/step - loss: 0.1302 - accuracy: 0.9641
Epoch 5/5
469/469 [==============================] - 14s 29ms/step - loss: 0.1168 - accuracy: 0.9663
79/79 [==============================] - 1s 9ms/step - loss: 0.0785 - accuracy: 0.9765
Test accuracy: 97.6%
+---------------------+------------------------------------------------------------------+
| Attribute | Value |
|---------------------+------------------------------------------------------------------|
| BUG_REPORT_URL | "https://bugs.launchpad.net/ubuntu/" |
| DISTRIB_CODENAME | bionic |
| DISTRIB_DESCRIPTION | "Ubuntu 18.04.5 LTS" |
| DISTRIB_ID | Ubuntu |
| DISTRIB_RELEASE | 18.04 |
| HOME_URL | "https://www.ubuntu.com/" |
| ID | ubuntu |
| ID_LIKE | debian |
| NAME | "Ubuntu" |
| PRETTY_NAME | "Ubuntu 18.04.5 LTS" |
| PRIVACY_POLICY_URL | "https://www.ubuntu.com/legal/terms-and-policies/privacy-policy" |
| SUPPORT_URL | "https://help.ubuntu.com/" |
| UBUNTU_CODENAME | bionic |
| VERSION | "18.04.5 LTS (Bionic Beaver)" |
| VERSION_CODENAME | bionic |
| VERSION_ID | "18.04" |
| cpu_count | 2 |
| mem.active | 1.2 GiB |
| mem.available | 11.6 GiB |
| mem.free | 9.8 GiB |
| mem.inactive | 1.4 GiB |
| mem.percent | 8.4 % |
| mem.total | 12.7 GiB |
| mem.used | 913.7 MiB |
| platform.version | #1 SMP Thu Jul 23 08:00:38 PDT 2020 |
| python | 3.6.9 (default, Oct 8 2020, 12:12:24) |
| | [GCC 8.4.0] |
| python.pip | 19.3.1 |
| python.version | 3.6.9 |
| sys.platform | linux |
| uname.machine | x86_64 |
| uname.node | 6609095905d1 |
| uname.processor | x86_64 |
| uname.release | 4.19.112+ |
| uname.system | Linux |
| uname.version | #1 SMP Thu Jul 23 08:00:38 PDT 2020 |
| user | collab |
+---------------------+------------------------------------------------------------------+
+-----------+----------+--------+--------+---------------------+-------+--------------+--------+-------+-------------------------------------+
| Name | Status | Time | Sum | Start | tag | Node | User | OS | Version |
|-----------+----------+--------+--------+---------------------+-------+--------------+--------+-------+-------------------------------------|
| data-load | failed | 0.549 | 0.549 | 2021-02-15 15:24:00 | | 6609095905d1 | collab | Linux | #1 SMP Thu Jul 23 08:00:38 PDT 2020 |
| compile | failed | 0.023 | 0.023 | 2021-02-15 15:24:01 | | 6609095905d1 | collab | Linux | #1 SMP Thu Jul 23 08:00:38 PDT 2020 |
| train | failed | 69.1 | 69.1 | 2021-02-15 15:24:01 | | 6609095905d1 | collab | Linux | #1 SMP Thu Jul 23 08:00:38 PDT 2020 |
| test | failed | 0.907 | 0.907 | 2021-02-15 15:25:10 | | 6609095905d1 | collab | Linux | #1 SMP Thu Jul 23 08:00:38 PDT 2020 |
+-----------+----------+--------+--------+---------------------+-------+--------------+--------+-------+-------------------------------------+
# csv,timer,status,time,sum,start,tag,uname.node,user,uname.system,platform.version
# csv,data-load,failed,0.549,0.549,2021-02-15 15:24:00,,6609095905d1,collab,Linux,#1 SMP Thu Jul 23 08:00:38 PDT 2020
# csv,compile,failed,0.023,0.023,2021-02-15 15:24:01,,6609095905d1,collab,Linux,#1 SMP Thu Jul 23 08:00:38 PDT 2020
# csv,train,failed,69.1,69.1,2021-02-15 15:24:01,,6609095905d1,collab,Linux,#1 SMP Thu Jul 23 08:00:38 PDT 2020
# csv,test,failed,0.907,0.907,2021-02-15 15:25:10,,6609095905d1,collab,Linux,#1 SMP Thu Jul 23 08:00:38 PDT 2020
Reference:
Last modified
June 17, 2021
: add aliasses (6b7beab5)