MNIST-RMM Classification on Google Colab
MNIST with Recurrent Neural Networks: Classification on Google Colab
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Prerequisites
Install the following packages
! pip3 install cloudmesh-installer
! pip3 install cloudmesh-common
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
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()
model.add(SimpleRNN(units=units,
dropout=dropout,
input_shape=input_shape, return_sequences=True))
model.add(SimpleRNN(units=units,
dropout=dropout,
return_sequences=True))
model.add(SimpleRNN(units=units,
dropout=dropout,
return_sequences=False))
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 #
=================================================================
simple_rnn (SimpleRNN) (None, 28, 256) 72960
_________________________________________________________________
simple_rnn_1 (SimpleRNN) (None, 28, 256) 131328
_________________________________________________________________
simple_rnn_2 (SimpleRNN) (None, 256) 131328
_________________________________________________________________
dense (Dense) (None, 10) 2570
_________________________________________________________________
activation (Activation) (None, 10) 0
=================================================================
Total params: 338,186
Trainable params: 338,186
Non-trainable params: 0
Train
StopWatch.start("train")
model.fit(x_train, y_train, epochs=1, batch_size=batch_size)
StopWatch.stop("train")
469/469 [==============================] - 125s 266ms/step - loss: 0.6794 - accuracy: 0.7783
<tensorflow.python.keras.callbacks.History at 0x7f35d4b104e0>
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 [==============================] - 7s 80ms/step - loss: 0.2581 - accuracy: 0.9181
Test accuracy: 91.8%
+---------------------+------------------------------------------------------------------+
| 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 | 1.3 GiB |
| mem.available | 11.6 GiB |
| mem.free | 9.7 GiB |
| mem.inactive | 1.5 GiB |
| mem.percent | 8.5 % |
| mem.total | 12.7 GiB |
| mem.used | 978.6 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 | 8f16b3b1f784 |
| 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 | 0.36 | 0.36 | 2021-02-18 15:16:12 | | 8f16b3b1f784 | collab | Linux | #1 SMP Thu Jul 23 08:00:38 PDT 2020 |
| data-pre-process | failed | 0.086 | 0.086 | 2021-02-18 15:16:12 | | 8f16b3b1f784 | collab | Linux | #1 SMP Thu Jul 23 08:00:38 PDT 2020 |
| compile | failed | 0.51 | 0.51 | 2021-02-18 15:16:12 | | 8f16b3b1f784 | collab | Linux | #1 SMP Thu Jul 23 08:00:38 PDT 2020 |
| train | failed | 126.612 | 126.612 | 2021-02-18 15:16:13 | | 8f16b3b1f784 | collab | Linux | #1 SMP Thu Jul 23 08:00:38 PDT 2020 |
| evaluate | failed | 6.798 | 6.798 | 2021-02-18 15:18:19 | | 8f16b3b1f784 | 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,0.36,0.36,2021-02-18 15:16:12,,8f16b3b1f784,collab,Linux,#1 SMP Thu Jul 23 08:00:38 PDT 2020
# csv,data-pre-process,failed,0.086,0.086,2021-02-18 15:16:12,,8f16b3b1f784,collab,Linux,#1 SMP Thu Jul 23 08:00:38 PDT 2020
# csv,compile,failed,0.51,0.51,2021-02-18 15:16:12,,8f16b3b1f784,collab,Linux,#1 SMP Thu Jul 23 08:00:38 PDT 2020
# csv,train,failed,126.612,126.612,2021-02-18 15:16:13,,8f16b3b1f784,collab,Linux,#1 SMP Thu Jul 23 08:00:38 PDT 2020
# csv,evaluate,failed,6.798,6.798,2021-02-18 15:18:19,,8f16b3b1f784,collab,Linux,#1 SMP Thu Jul 23 08:00:38 PDT 2020
Reference:
Last modified
June 17, 2021
: add aliasses (6b7beab5)