MLP + LSTM with MNIST on Google Colab
MLP + LSTM with MNIST 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 with LSTM.
Pre-requisites
Install the following Python packages
- cloudmesh-installer
- cloudmesh-common
pip3 install cloudmesh-installer
pip3 install cloudmesh-common
Sample MLP + LSTM with Tensorflow Keras
Import Libraries
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Activation, SimpleRNN, InputLayer, LSTM, Dropout
from tensorflow.keras.utils import to_categorical, plot_model
from tensorflow.keras.datasets import mnist
from cloudmesh.common.StopWatch import StopWatch
Download Data and Pre-Process
StopWatch.start("data-load")
(x_train, y_train), (x_test, y_test) = mnist.load_data()
StopWatch.stop("data-load")
StopWatch.start("data-pre-process")
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]
x_train = np.reshape(x_train,[-1, image_size, image_size])
x_test = np.reshape(x_test,[-1, image_size, image_size])
x_train = x_train.astype('float32') / 255
x_test = x_test.astype('float32') / 255
StopWatch.stop("data-pre-process")
input_shape = (image_size, image_size)
batch_size = 128
units = 256
dropout = 0.2
Define Model
StopWatch.start("compile")
model = Sequential()
# LSTM Layers
model.add(LSTM(units=units,
input_shape=input_shape,
return_sequences=True))
model.add(LSTM(units=units,
dropout=dropout,
return_sequences=True))
model.add(LSTM(units=units,
dropout=dropout,
return_sequences=False))
# MLP Layers
model.add(Dense(units))
model.add(Activation('relu'))
model.add(Dropout(dropout))
model.add(Dense(units))
model.add(Activation('relu'))
model.add(Dropout(dropout))
# Softmax_layer
model.add(Dense(num_labels))
model.add(Activation('softmax'))
model.summary()
plot_model(model, to_file='rnn-mnist.png', show_shapes=True)
model.compile(loss='categorical_crossentropy',
optimizer='sgd',
metrics=['accuracy'])
StopWatch.stop("compile")
Model: "sequential"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
lstm (LSTM) (None, 28, 256) 291840
_________________________________________________________________
lstm_1 (LSTM) (None, 28, 256) 525312
_________________________________________________________________
lstm_2 (LSTM) (None, 256) 525312
_________________________________________________________________
dense (Dense) (None, 256) 65792
_________________________________________________________________
activation (Activation) (None, 256) 0
_________________________________________________________________
dropout (Dropout) (None, 256) 0
_________________________________________________________________
dense_1 (Dense) (None, 256) 65792
_________________________________________________________________
activation_1 (Activation) (None, 256) 0
_________________________________________________________________
dropout_1 (Dropout) (None, 256) 0
_________________________________________________________________
dense_2 (Dense) (None, 10) 2570
_________________________________________________________________
activation_2 (Activation) (None, 10) 0
=================================================================
Total params: 1,476,618
Trainable params: 1,476,618
Non-trainable params: 0
Train
StopWatch.start("train")
model.fit(x_train, y_train, epochs=30, batch_size=batch_size)
StopWatch.stop("train")
469/469 [==============================] - 378s 796ms/step - loss: 2.2689 - accuracy: 0.2075
Test
StopWatch.start("evaluate")
loss, acc = model.evaluate(x_test, y_test, batch_size=batch_size)
print("\nTest accuracy: %.1f%%" % (100.0 * acc))
StopWatch.stop("evaluate")
StopWatch.benchmark()
79/79 [==============================] - 1s 7ms/step - loss: 2.2275 - accuracy: 0.3120
Test accuracy: 31.2%
+---------------------+------------------------------------------------------------------+
| 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 | 1.9 GiB |
| mem.available | 10.7 GiB |
| mem.free | 7.3 GiB |
| mem.inactive | 3.0 GiB |
| mem.percent | 15.6 % |
| mem.total | 12.7 GiB |
| mem.used | 2.3 GiB |
| 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 | 9810ccb69d08 |
| 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 | 0.61 | 0.61 | 2021-02-21 21:35:06 | | 9810ccb69d08 | collab | Linux | #1 SMP Thu Jul 23 08:00:38 PDT 2020 |
| data-pre-process | failed | 0.076 | 0.076 | 2021-02-21 21:35:07 | | 9810ccb69d08 | collab | Linux | #1 SMP Thu Jul 23 08:00:38 PDT 2020 |
| compile | failed | 6.445 | 6.445 | 2021-02-21 21:35:07 | | 9810ccb69d08 | collab | Linux | #1 SMP Thu Jul 23 08:00:38 PDT 2020 |
| train | failed | 17.171 | 17.171 | 2021-02-21 21:35:13 | | 9810ccb69d08 | collab | Linux | #1 SMP Thu Jul 23 08:00:38 PDT 2020 |
| evaluate | failed | 1.442 | 1.442 | 2021-02-21 21:35:31 | | 9810ccb69d08 | 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,0.61,0.61,2021-02-21 21:35:06,,9810ccb69d08,collab,Linux,#1 SMP Thu Jul 23 08:00:38 PDT 2020
# csv,data-pre-process,failed,0.076,0.076,2021-02-21 21:35:07,,9810ccb69d08,collab,Linux,#1 SMP Thu Jul 23 08:00:38 PDT 2020
# csv,compile,failed,6.445,6.445,2021-02-21 21:35:07,,9810ccb69d08,collab,Linux,#1 SMP Thu Jul 23 08:00:38 PDT 2020
# csv,train,failed,17.171,17.171,2021-02-21 21:35:13,,9810ccb69d08,collab,Linux,#1 SMP Thu Jul 23 08:00:38 PDT 2020
# csv,evaluate,failed,1.442,1.442,2021-02-21 21:35:31,,9810ccb69d08,collab,Linux,#1 SMP Thu Jul 23 08:00:38 PDT 2020
Reference:
Last modified
June 17, 2021
: add aliasses (6b7beab5)