! python --version

for i in range(10):
  print(i)

list_items = ['a', 'b', 'c', 'd', 'e']

list_items[2]

list_items.append('f')

list_items

list_items.remove('a')

list_items

dictionary_items = {'a':1, 'b': 2, 'c': 3}

dictionary_items['b']

dictionary_items['c'] = 4

dictionary_items

del dictionary_items['a'] 

dictionary_items

x = 10
y = 20 
z = 30

x > y 

x < z

z == x

if x < z:
  print("This is True")

if x > z:
  print("This is True")
else:
  print("This is False")  

k = x * y * z
k

j = x + y + z
j

m = x -y 
m

n = x / z
n

import numpy as np

a = np.random.rand(100)
a.shape

b = a.reshape(10,10)
b.shape

c = b * 10
c[0]

c = np.mean(b,axis=1)
c.shape

print(c)

pip3 install cloudmesh-installer
pip3 install cloudmesh-common

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

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

StopWatch.start("compile")
strategy = tf.distribute.MirroredStrategy()
with strategy.scope():
  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)
  
  print("Number of devices: {}".format(strategy.num_replicas_in_sync))

  model.compile(loss='categorical_crossentropy',
                optimizer='sgd',
                metrics=['accuracy'])
StopWatch.stop("compile")

Model: "sequential_2"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
lstm_6 (LSTM)                (None, 28, 256)           291840    
_________________________________________________________________
lstm_7 (LSTM)                (None, 28, 256)           525312    
_________________________________________________________________
lstm_8 (LSTM)                (None, 256)               525312    
_________________________________________________________________
dense_6 (Dense)              (None, 256)               65792     
_________________________________________________________________
activation_6 (Activation)    (None, 256)               0         
_________________________________________________________________
dropout_4 (Dropout)          (None, 256)               0         
_________________________________________________________________
dense_7 (Dense)              (None, 256)               65792     
_________________________________________________________________
activation_7 (Activation)    (None, 256)               0         
_________________________________________________________________
dropout_5 (Dropout)          (None, 256)               0         
_________________________________________________________________
dense_8 (Dense)              (None, 10)                2570      
_________________________________________________________________
activation_8 (Activation)    (None, 10)                0         
=================================================================
Total params: 1,476,618
Trainable params: 1,476,618
Non-trainable params: 0
_________________________________________________________________
INFO:tensorflow:Using MirroredStrategy with devices ('/job:localhost/replica:0/task:0/device:GPU:0',)
Number of devices: 1
INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).
INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).
INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).
INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).

StopWatch.start("train")
model.fit(x_train, y_train, epochs=30, batch_size=batch_size)
StopWatch.stop("train")

Epoch 1/30
469/469 [==============================] - 7s 16ms/step - loss: 2.0427 - accuracy: 0.2718
Epoch 2/30
469/469 [==============================] - 7s 16ms/step - loss: 1.6934 - accuracy: 0.4007
Epoch 3/30
469/469 [==============================] - 7s 16ms/step - loss: 1.2997 - accuracy: 0.5497
...
Epoch 28/30
469/469 [==============================] - 8s 17ms/step - loss: 0.1175 - accuracy: 0.9640
Epoch 29/30
469/469 [==============================] - 8s 17ms/step - loss: 0.1158 - accuracy: 0.9645
Epoch 30/30
469/469 [==============================] - 8s 17ms/step - loss: 0.1098 - accuracy: 0.9661

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 [==============================] - 3s 9ms/step - loss: 0.0898 - accuracy: 0.9719

Test accuracy: 97.2%

+---------------------+------------------------------------------------------------------+
| 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          | 2.4 GiB                                                          |
| mem.available       | 10.3 GiB                                                         |
| mem.free            | 4.5 GiB                                                          |
| mem.inactive        | 5.4 GiB                                                          |
| mem.percent         | 18.6 %                                                           |
| mem.total           | 12.7 GiB                                                         |
| mem.used            | 3.3 GiB                                                          |
| platform.version    | #1 SMP Thu Jul 23 08:00:38 PDT 2020                              |
| python              | 3.7.10 (default, Feb 20 2021, 21:17:23)                          |
|                     | [GCC 7.5.0]                                                      |
| python.pip          | 19.3.1                                                           |
| python.version      | 3.7.10                                                           |
| sys.platform        | linux                                                            |
| uname.machine       | x86_64                                                           |
| uname.node          | b39e0899c1f8                                                     |
| 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   |   0.473 |   0.473 | 2021-03-07 11:34:03 |       | b39e0899c1f8 | collab | Linux | #1 SMP Thu Jul 23 08:00:38 PDT 2020 |
| data-pre-process | failed   |   0.073 |   0.073 | 2021-03-07 11:34:03 |       | b39e0899c1f8 | collab | Linux | #1 SMP Thu Jul 23 08:00:38 PDT 2020 |
| compile          | failed   |   0.876 |   7.187 | 2021-03-07 11:38:05 |       | b39e0899c1f8 | collab | Linux | #1 SMP Thu Jul 23 08:00:38 PDT 2020 |
| train            | failed   | 229.341 | 257.023 | 2021-03-07 11:38:44 |       | b39e0899c1f8 | collab | Linux | #1 SMP Thu Jul 23 08:00:38 PDT 2020 |
| evaluate         | failed   |   2.659 |   4.25  | 2021-03-07 11:44:54 |       | b39e0899c1f8 | 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,0.473,0.473,2021-03-07 11:34:03,,b39e0899c1f8,collab,Linux,#1 SMP Thu Jul 23 08:00:38 PDT 2020
# csv,data-pre-process,failed,0.073,0.073,2021-03-07 11:34:03,,b39e0899c1f8,collab,Linux,#1 SMP Thu Jul 23 08:00:38 PDT 2020
# csv,compile,failed,0.876,7.187,2021-03-07 11:38:05,,b39e0899c1f8,collab,Linux,#1 SMP Thu Jul 23 08:00:38 PDT 2020
# csv,train,failed,229.341,257.023,2021-03-07 11:38:44,,b39e0899c1f8,collab,Linux,#1 SMP Thu Jul 23 08:00:38 PDT 2020
# csv,evaluate,failed,2.659,4.25,2021-03-07 11:44:54,,b39e0899c1f8,collab,Linux,#1 SMP Thu Jul 23 08:00:38 PDT 2020

pip3 install cloudmesh-installer
pip3 install cloudmesh-common

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

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

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

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

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

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

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

(x_train, y_train), (x_test, y_test) = mnist.load_data()

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

# Create Model
# network parameters
batch_size = 4
hidden_units = 64

model = Sequential()
model.add(Dense(hidden_units, input_dim=input_size))
model.add(Dense(num_labels))
model.add(Activation('softmax'))
model.summary()
plot_model(model, to_file='mlp-mnist.png', show_shapes=True)

model.compile(loss='categorical_crossentropy',
              optimizer='adam',
              metrics=['accuracy'])

model.fit(x_train, y_train, epochs=1, batch_size=batch_size)

loss, acc = model.evaluate(x_test, y_test, batch_size=batch_size)
print("\nTest accuracy: %.1f%%" % (100.0 * acc))

import numpy as np
import torch
import torchvision
import matplotlib.pyplot as plt
from torchvision import datasets, transforms
from torch import nn
from torch import optim
from time import time
import os
from google.colab import drive

# Data transformation function 
transform = transforms.Compose([transforms.ToTensor(),
                              transforms.Normalize((0.5,), (0.5,)),
                              ])

# DataSet
train_data_set = datasets.MNIST('drive/My Drive/mnist/data/', download=True, train=True, transform=transform)
validation_data_set = datasets.MNIST('drive/My Drive/mnist/data/', download=True, train=False, transform=transform)

# DataLoader
train_loader = torch.utils.data.DataLoader(train_data_set, batch_size=32, shuffle=True)
validation_loader = torch.utils.data.DataLoader(validation_data_set, batch_size=32, shuffle=True)

input_size = 784
hidden_sizes = [128, 128, 64, 64]
output_size = 10

model = nn.Sequential(nn.Linear(input_size, hidden_sizes[0]),
                      nn.ReLU(),
                      nn.Linear(hidden_sizes[0], hidden_sizes[1]),
                      nn.ReLU(),
                      nn.Linear(hidden_sizes[1], hidden_sizes[2]),
                      nn.ReLU(),
                      nn.Linear(hidden_sizes[2], hidden_sizes[3]),
                      nn.ReLU(),
                      nn.Linear(hidden_sizes[3], output_size),
                      nn.LogSoftmax(dim=1))

                      
print(model)

Sequential(
  (0): Linear(in_features=784, out_features=128, bias=True)
  (1): ReLU()
  (2): Linear(in_features=128, out_features=128, bias=True)
  (3): ReLU()
  (4): Linear(in_features=128, out_features=64, bias=True)
  (5): ReLU()
  (6): Linear(in_features=64, out_features=64, bias=True)
  (7): ReLU()
  (8): Linear(in_features=64, out_features=10, bias=True)
  (9): LogSoftmax(dim=1)
)

criterion = nn.NLLLoss()
optimizer = optim.SGD(model.parameters(), lr=0.003, momentum=0.9)

epochs = 5

for epoch in range(epochs):
    loss_per_epoch = 0
    for images, labels in train_loader:
        images = images.view(images.shape[0], -1)
    
        # Gradients cleared per batch
        optimizer.zero_grad()
        
        # Pass input to the model
        output = model(images)
        # Calculate loss after training compared to labels
        loss = criterion(output, labels)
        
        # backpropagation 
        loss.backward()
        
        # optimizer step to update the weights
        optimizer.step()
        
        loss_per_epoch += loss.item()
    average_loss = loss_per_epoch / len(train_loader)
    print("Epoch {} - Training loss: {}".format(epoch, average_loss))

Epoch 0 - Training loss: 1.3052690227402808
Epoch 1 - Training loss: 0.33809808635317695
Epoch 2 - Training loss: 0.22927882223685922
Epoch 3 - Training loss: 0.16807103878669521
Epoch 4 - Training loss: 0.1369301250545995

correct_predictions, all_count = 0, 0
# enumerate data from the data validation loader (loads a batch at a time)
for batch_id, (images,labels) in enumerate(validation_loader):
  for i in range(len(labels)):
    img = images[i].view(1, 784)
    # at prediction stage, only feed-forward calculation is required. 
    with torch.no_grad():
        logps = model(img)

    # Output layer of the network uses a LogSoftMax layer
    # Hence the probability must be calculated with the exponential values. 
    # The final layer returns an array of probabilities for each label
    # Pick the maximum probability and the corresponding index
    # The corresponding index is the predicted label 
    ps = torch.exp(logps)
    probab = list(ps.numpy()[0])
    pred_label = probab.index(max(probab))
    true_label = labels.numpy()[i]
    if(true_label == pred_label):
      correct_predictions += 1
    all_count += 1

print(f"Model Accuracy {(correct_predictions/all_count) * 100} %")

Model Accuracy 95.95 %

! pip3 install cloudmesh-installer
! pip3 install cloudmesh-common

import tensorflow as tf
from keras.layers import Dense, Input
from keras.layers import Conv2D, Flatten
from keras.layers import Reshape, Conv2DTranspose
from keras.models import Model
from keras.datasets import mnist
from keras.utils import plot_model
from keras import backend as K

import numpy as np
import matplotlib.pyplot as plt

(x_train, y_train), (x_test, y_test) = mnist.load_data()

image_size = x_train.shape[1]
x_train = np.reshape(x_train, [-1, image_size, image_size, 1])
x_test = np.reshape(x_test, [-1, image_size, image_size, 1])
x_train = x_train.astype('float32') / 255
x_test = x_test.astype('float32') / 255

input_shape = (image_size, image_size, 1)
batch_size = 32
kernel_size = 3
latent_dim = 16
hidden_units = [32, 64]

Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz
11493376/11490434 [==============================] - 0s 0us/step

inputs = Input(shape=input_shape, name='encoder_input')
x = inputs
x = Dense(hidden_units[0], activation='relu')(x)
x = Dense(hidden_units[1], activation='relu')(x)

shape = K.int_shape(x)

# generate latent vector
x = Flatten()(x)
latent = Dense(latent_dim, name='latent_vector')(x)

# instantiate encoder model
encoder = Model(inputs,
                latent,
                name='encoder')
encoder.summary()
plot_model(encoder,
           to_file='encoder.png',
           show_shapes=True)


latent_inputs = Input(shape=(latent_dim,), name='decoder_input')
x = Dense(shape[1] * shape[2] * shape[3])(latent_inputs)
x = Reshape((shape[1], shape[2], shape[3]))(x)
x = Dense(hidden_units[0], activation='relu')(x)
x = Dense(hidden_units[1], activation='relu')(x)

outputs = Dense(1, activation='relu')(x)

decoder = Model(latent_inputs, outputs, name='decoder')
decoder.summary()
plot_model(decoder, to_file='decoder.png', show_shapes=True)

autoencoder = Model(inputs,
                    decoder(encoder(inputs)),
                    name='autoencoder')
autoencoder.summary()
plot_model(autoencoder,
           to_file='autoencoder.png',
           show_shapes=True)

autoencoder.compile(loss='mse', optimizer='adam')

Model: "encoder"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
encoder_input (InputLayer)   [(None, 28, 28, 1)]       0         
_________________________________________________________________
dense_2 (Dense)              (None, 28, 28, 32)        64        
_________________________________________________________________
dense_3 (Dense)              (None, 28, 28, 64)        2112      
_________________________________________________________________
flatten_1 (Flatten)          (None, 50176)             0         
_________________________________________________________________
latent_vector (Dense)        (None, 16)                802832    
=================================================================
Total params: 805,008
Trainable params: 805,008
Non-trainable params: 0
_________________________________________________________________
Model: "decoder"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
decoder_input (InputLayer)   [(None, 16)]              0         
_________________________________________________________________
dense_4 (Dense)              (None, 50176)             852992    
_________________________________________________________________
reshape (Reshape)            (None, 28, 28, 64)        0         
_________________________________________________________________
dense_5 (Dense)              (None, 28, 28, 32)        2080      
_________________________________________________________________
dense_6 (Dense)              (None, 28, 28, 64)        2112      
_________________________________________________________________
dense_7 (Dense)              (None, 28, 28, 1)         65        
=================================================================
Total params: 857,249
Trainable params: 857,249
Non-trainable params: 0
_________________________________________________________________
Model: "autoencoder"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
encoder_input (InputLayer)   [(None, 28, 28, 1)]       0         
_________________________________________________________________
encoder (Functional)         (None, 16)                805008    
_________________________________________________________________
decoder (Functional)         (None, 28, 28, 1)         857249    
=================================================================
Total params: 1,662,257
Trainable params: 1,662,257
Non-trainable params: 0

autoencoder.fit(x_train,
                x_train,
                validation_data=(x_test, x_test),
                epochs=1,
                batch_size=batch_size)

1875/1875 [==============================] - 112s 60ms/step - loss: 0.0268 - val_loss: 0.0131

<tensorflow.python.keras.callbacks.History at 0x7f3ecb2e0be0>

x_decoded = autoencoder.predict(x_test)

79/79 [==============================] - 7s 80ms/step - loss: 0.2581 - accuracy: 0.9181

Test accuracy: 91.8%

imgs = np.concatenate([x_test[:8], x_decoded[:8]])
imgs = imgs.reshape((4, 4, image_size, image_size))
imgs = np.vstack([np.hstack(i) for i in imgs])
plt.figure()
plt.axis('off')
plt.title('Input: 1st 2 rows, Decoded: last 2 rows')
plt.imshow(imgs, interpolation='none', cmap='gray')
plt.savefig('input_and_decoded.png')
plt.show()

! pip3 install cloudmesh-installer
! pip3 install cloudmesh-common

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import numpy as np
from keras.models import Sequential
from keras.layers import Activation, Dense, Dropout
from keras.layers import Conv2D, MaxPooling2D, Flatten, AveragePooling2D
from keras.utils import to_categorical, plot_model
from keras.datasets import mnist

(x_train, y_train), (x_test, y_test) = mnist.load_data()

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, 1])
x_test = np.reshape(x_test,[-1, image_size, image_size, 1])
x_train = x_train.astype('float32') / 255
x_test = x_test.astype('float32') / 255

input_shape = (image_size, image_size, 1)
print(input_shape)
batch_size = 128
kernel_size = 3
pool_size = 2
filters = 64
dropout = 0.2

Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz
11493376/11490434 [==============================] - 0s 0us/step
(28, 28, 1)

model = Sequential()
model.add(Conv2D(filters=filters,
                 kernel_size=kernel_size,
                 activation='relu',
                 input_shape=input_shape,
                 padding='same'))
model.add(MaxPooling2D(pool_size))
model.add(Conv2D(filters=filters,
                 kernel_size=kernel_size,
                 activation='relu',
                 input_shape=input_shape,
                 padding='same'))
model.add(MaxPooling2D(pool_size))
model.add(Conv2D(filters=filters,
                 kernel_size=kernel_size,
                 activation='relu',
                 padding='same'))
model.add(MaxPooling2D(pool_size))
model.add(Conv2D(filters=filters,
                 kernel_size=kernel_size,
                 activation='relu'))
model.add(Flatten())
model.add(Dropout(dropout))
model.add(Dense(num_labels))
model.add(Activation('softmax'))
model.summary()
plot_model(model, to_file='cnn-mnist.png', show_shapes=True)

Model: "sequential_1"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
conv2d_4 (Conv2D)            (None, 28, 28, 64)        640       
_________________________________________________________________
max_pooling2d_3 (MaxPooling2 (None, 14, 14, 64)        0         
_________________________________________________________________
conv2d_5 (Conv2D)            (None, 14, 14, 64)        36928     
_________________________________________________________________
max_pooling2d_4 (MaxPooling2 (None, 7, 7, 64)          0         
_________________________________________________________________
conv2d_6 (Conv2D)            (None, 7, 7, 64)          36928     
_________________________________________________________________
max_pooling2d_5 (MaxPooling2 (None, 3, 3, 64)          0         
_________________________________________________________________
conv2d_7 (Conv2D)            (None, 1, 1, 64)          36928     
_________________________________________________________________
flatten_1 (Flatten)          (None, 64)                0         
_________________________________________________________________
dropout_1 (Dropout)          (None, 64)                0         
_________________________________________________________________
dense_1 (Dense)              (None, 10)                650       
_________________________________________________________________
activation_1 (Activation)    (None, 10)                0         
=================================================================
Total params: 112,074
Trainable params: 112,074
Non-trainable params: 0
_________________________________________________________________

model.compile(loss='categorical_crossentropy',
              optimizer='adam',
              metrics=['accuracy'])
# train the network
model.fit(x_train, y_train, epochs=10, batch_size=batch_size)

469/469 [==============================] - 125s 266ms/step - loss: 0.6794 - accuracy: 0.7783

<tensorflow.python.keras.callbacks.History at 0x7f35d4b104e0>

loss, acc = model.evaluate(x_test, y_test, batch_size=batch_size)
print("\nTest accuracy: %.1f%%" % (100.0 * acc))

79/79 [==============================] - 6s 68ms/step - loss: 0.0608 - accuracy: 0.9813

Test accuracy: 98.1%

pip3 install cloudmesh-installer
pip3 install cloudmesh-common

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
from tensorflow.keras.utils import to_categorical, plot_model
from tensorflow.keras.datasets import mnist
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")


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

StopWatch.start("compile")
model = Sequential()
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))
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_1"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
lstm_3 (LSTM)                (None, 28, 256)           291840    
_________________________________________________________________
lstm_4 (LSTM)                (None, 28, 256)           525312    
_________________________________________________________________
lstm_5 (LSTM)                (None, 256)               525312    
_________________________________________________________________
dense_1 (Dense)              (None, 10)                2570      
_________________________________________________________________
activation_1 (Activation)    (None, 10)                0         
=================================================================
Total params: 1,345,034
Trainable params: 1,345,034
Non-trainable params: 0

StopWatch.start("train")
model.fit(x_train, y_train, epochs=1, batch_size=batch_size)
StopWatch.stop("train")

469/469 [==============================] - 378s 796ms/step - loss: 2.2689 - accuracy: 0.2075

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 [==============================] - 22s 260ms/step - loss: 1.9646 - accuracy: 0.3505

Test accuracy: 35.0%

+---------------------+------------------------------------------------------------------+
| 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          | 1.5 GiB                                                          |
| mem.available       | 11.4 GiB                                                         |
| mem.free            | 9.3 GiB                                                          |
| mem.inactive        | 1.7 GiB                                                          |
| mem.percent         | 10.4 %                                                           |
| mem.total           | 12.7 GiB                                                         |
| mem.used            | 1.3 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          | 351ef0f61c92                                                     |
| 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   |   0.354 |   0.967 | 2021-02-18 15:27:21 |       | 351ef0f61c92 | collab | Linux | #1 SMP Thu Jul 23 08:00:38 PDT 2020 |
| data-pre-process | failed   |   0.098 |   0.198 | 2021-02-18 15:27:21 |       | 351ef0f61c92 | collab | Linux | #1 SMP Thu Jul 23 08:00:38 PDT 2020 |
| compile          | failed   |   0.932 |   2.352 | 2021-02-18 15:27:23 |       | 351ef0f61c92 | collab | Linux | #1 SMP Thu Jul 23 08:00:38 PDT 2020 |
| train            | failed   | 377.842 | 377.842 | 2021-02-18 15:27:26 |       | 351ef0f61c92 | collab | Linux | #1 SMP Thu Jul 23 08:00:38 PDT 2020 |
| evaluate         | failed   |  21.689 |  21.689 | 2021-02-18 15:33:44 |       | 351ef0f61c92 | 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,0.354,0.967,2021-02-18 15:27:21,,351ef0f61c92,collab,Linux,#1 SMP Thu Jul 23 08:00:38 PDT 2020
# csv,data-pre-process,failed,0.098,0.198,2021-02-18 15:27:21,,351ef0f61c92,collab,Linux,#1 SMP Thu Jul 23 08:00:38 PDT 2020
# csv,compile,failed,0.932,2.352,2021-02-18 15:27:23,,351ef0f61c92,collab,Linux,#1 SMP Thu Jul 23 08:00:38 PDT 2020
# csv,train,failed,377.842,377.842,2021-02-18 15:27:26,,351ef0f61c92,collab,Linux,#1 SMP Thu Jul 23 08:00:38 PDT 2020
# csv,evaluate,failed,21.689,21.689,2021-02-18 15:33:44,,351ef0f61c92,collab,Linux,#1 SMP Thu Jul 23 08:00:38 PDT 2020

pip3 install cloudmesh-installer
pip3 install cloudmesh-common

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

! pip3 install cloudmesh-installer
! pip3 install cloudmesh-common

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

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

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

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>

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