import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import matplotlib.pyplot as plt
import warnings
import os
import tensorflow as tf
warnings.filterwarnings('ignore')
import shutil
from random import shuffle

Unzipping Data

!wget https://cainvas-static.s3.amazonaws.com/media/user_data/SiddharthGan/archive_1.zip

!unzip -qo archive_1.zip

# zip folder is not needed anymore
!rm archive_1.zip

--2021-08-30 08:03:32--  https://cainvas-static.s3.amazonaws.com/media/user_data/SiddharthGan/archive_1.zip
Resolving cainvas-static.s3.amazonaws.com (cainvas-static.s3.amazonaws.com)... 52.219.62.120
Connecting to cainvas-static.s3.amazonaws.com (cainvas-static.s3.amazonaws.com)|52.219.62.120|:443... connected.
HTTP request sent, awaiting response... 200 OK
Length: 411916 (402K) [application/zip]
Saving to: ‘archive_1.zip’

archive_1.zip       100%[===================>] 402.26K  --.-KB/s    in 0.003s  

2021-08-30 08:03:33 (120 MB/s) - ‘archive_1.zip’ saved [411916/411916]

shapes = ['triangles', 'circles', 'squares']

path= 'shapes/'

files= []
result= []

for shape in shapes:
    new_path= path+shape
    
    for file in os.listdir(new_path):
        files.append(os.path.join(new_path,file))
        result.append(shape)

len(files)

300

len(result)

300

files[:5]

['shapes/triangles/drawing(3).png',
 'shapes/triangles/drawing(16).png',
 'shapes/triangles/drawing(71).png',
 'shapes/triangles/drawing(68).png',
 'shapes/triangles/drawing(75).png']

from skimage import color
images=[]
for file in files:
    img= plt.imread(file)
    img= color.rgb2gray(img)
    img= img.ravel()
    images.append(img)

len(images)

300

Data Visualsation

df= pd.DataFrame(images)

df.head()

	0	1	2	3	4	5	6	7	8	9	...	774	775	776	777	778	779	780	781	782	783
0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	...	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
1	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	...	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
2	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	...	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
3	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	...	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0
4	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	...	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0

5 rows × 784 columns

df['result']= result
df.head()

	0	1	2	3	4	5	6	7	8	9	...	775	776	777	778	779	780	781	782	783	result
0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	...	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	triangles
1	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	...	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	triangles
2	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	...	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	triangles
3	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	...	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	triangles
4	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	...	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	triangles

5 rows × 785 columns

from sklearn.utils import shuffle

df= shuffle(df)
df.head()

	0	1	2	3	4	5	6	7	8	9	...	775	776	777	778	779	780	781	782	783	result
149	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	...	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	circles
234	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	...	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	squares
195	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	...	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	circles
34	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	...	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	triangles
282	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	...	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	squares

5 rows × 785 columns

df['result']= df['result'].replace({'circles':0, 'triangles':1, 'squares':2})
df.head()

	0	1	2	3	4	5	6	7	8	9	...	775	776	777	778	779	780	781	782	783	result
149	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	...	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	0
234	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	...	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	2
195	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	...	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	0
34	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	...	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1
282	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	...	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	1.0	2

5 rows × 785 columns

Dropping useless column

y= df['result']
X= df.drop('result', axis=1)

X= X.values.reshape(-1,28,28,1)

Uniform Type Conversion

from tensorflow.keras.utils import to_categorical

y= to_categorical(y)
y.astype('int32')

array([[1, 0, 0],
       [0, 0, 1],
       [1, 0, 0],
       [0, 1, 0],
       [0, 0, 1],
       [0, 0, 1],
       [0, 1, 0],
       [0, 1, 0],
       [0, 1, 0],
       [0, 1, 0],
       [0, 0, 1],
       [1, 0, 0],
       [1, 0, 0],
       [0, 0, 1],
       [1, 0, 0],
       [0, 0, 1],
       [1, 0, 0],
       [0, 1, 0],
       [1, 0, 0],
       [0, 1, 0],
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       [1, 0, 0],
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       [0, 0, 1],
       [0, 1, 0],
       [0, 1, 0],
       [0, 1, 0],
       [0, 0, 1],
       [0, 1, 0],
       [0, 0, 1],
       [0, 1, 0],
       [0, 1, 0],
       [1, 0, 0],
       [1, 0, 0],
       [0, 0, 1],
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       [1, 0, 0],
       [0, 1, 0],
       [0, 1, 0],
       [1, 0, 0],
       [0, 1, 0],
       [1, 0, 0],
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       [0, 0, 1],
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       [0, 1, 0],
       [0, 0, 1],
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       [0, 0, 1],
       [0, 1, 0],
       [1, 0, 0],
       [1, 0, 0],
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       [0, 1, 0],
       [0, 1, 0],
       [0, 0, 1],
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       [0, 0, 1],
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       [0, 1, 0],
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       [1, 0, 0],
       [0, 1, 0],
       [1, 0, 0],
       [0, 1, 0],
       [0, 1, 0],
       [1, 0, 0],
       [1, 0, 0],
       [0, 0, 1],
       [0, 0, 1],
       [0, 1, 0],
       [0, 0, 1],
       [0, 0, 1],
       [1, 0, 0],
       [0, 0, 1],
       [0, 1, 0],
       [1, 0, 0],
       [1, 0, 0],
       [0, 0, 1],
       [1, 0, 0],
       [0, 1, 0],
       [0, 0, 1],
       [1, 0, 0],
       [0, 1, 0],
       [0, 0, 1],
       [0, 0, 1],
       [0, 1, 0],
       [0, 1, 0],
       [0, 1, 0],
       [0, 0, 1],
       [1, 0, 0],
       [1, 0, 0],
       [0, 1, 0],
       [0, 0, 1],
       [0, 1, 0],
       [1, 0, 0],
       [0, 1, 0],
       [1, 0, 0],
       [1, 0, 0],
       [0, 0, 1],
       [0, 1, 0],
       [1, 0, 0],
       [0, 1, 0],
       [0, 0, 1],
       [0, 0, 1],
       [0, 1, 0],
       [0, 1, 0],
       [0, 0, 1],
       [1, 0, 0],
       [0, 0, 1]], dtype=int32)

Train Test Split

from sklearn.model_selection import train_test_split

X_train, X_test, y_train,y_test= train_test_split(X,y, test_size=0.2, random_state=42)

Model Architecture

from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Flatten, Dropout, Conv2D, BatchNormalization, MaxPool2D

model= Sequential()
model.add(Conv2D(32, (3,3), activation='relu', padding='same', input_shape=(28,28,1)))
model.add(Conv2D(32, (3,3), activation='relu'))

model.add(MaxPool2D(2,2))
model.add(Dropout(0.25))

model.add(Conv2D(64, (3,3), activation='relu'))
model.add(MaxPool2D(2,2))

model.add(Flatten())
model.add(Dense(64, activation='relu'))
model.add(Dropout(0.25))
model.add(Dense(3, activation='softmax'))

model.summary()

Model: "sequential"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
conv2d (Conv2D)              (None, 28, 28, 32)        320       
_________________________________________________________________
conv2d_1 (Conv2D)            (None, 26, 26, 32)        9248      
_________________________________________________________________
max_pooling2d (MaxPooling2D) (None, 13, 13, 32)        0         
_________________________________________________________________
dropout (Dropout)            (None, 13, 13, 32)        0         
_________________________________________________________________
conv2d_2 (Conv2D)            (None, 11, 11, 64)        18496     
_________________________________________________________________
max_pooling2d_1 (MaxPooling2 (None, 5, 5, 64)          0         
_________________________________________________________________
flatten (Flatten)            (None, 1600)              0         
_________________________________________________________________
dense (Dense)                (None, 64)                102464    
_________________________________________________________________
dropout_1 (Dropout)          (None, 64)                0         
_________________________________________________________________
dense_1 (Dense)              (None, 3)                 195       
=================================================================
Total params: 130,723
Trainable params: 130,723
Non-trainable params: 0
_________________________________________________________________

from tensorflow.keras.callbacks import ModelCheckpoint

model_save= ModelCheckpoint('best.h5', save_best_only=True, monitor='val_accuracy')

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

Training the model

history= model.fit(X_train, y_train, batch_size=20, epochs=100, verbose=1, validation_data=(X_test, y_test), callbacks=[model_save])

Epoch 1/100
12/12 [==============================] - 0s 17ms/step - loss: 1.1123 - accuracy: 0.3042 - val_loss: 1.0959 - val_accuracy: 0.3167
Epoch 2/100
12/12 [==============================] - 0s 5ms/step - loss: 1.0974 - accuracy: 0.3417 - val_loss: 1.0921 - val_accuracy: 0.4333
Epoch 3/100
12/12 [==============================] - 0s 4ms/step - loss: 1.0963 - accuracy: 0.3333 - val_loss: 1.0907 - val_accuracy: 0.3500
Epoch 4/100
12/12 [==============================] - 0s 5ms/step - loss: 1.0819 - accuracy: 0.4333 - val_loss: 1.0654 - val_accuracy: 0.5167
Epoch 5/100
12/12 [==============================] - 0s 4ms/step - loss: 1.0621 - accuracy: 0.4542 - val_loss: 1.0147 - val_accuracy: 0.4833
Epoch 6/100
12/12 [==============================] - 0s 6ms/step - loss: 0.9976 - accuracy: 0.5167 - val_loss: 0.9249 - val_accuracy: 0.5333
Epoch 7/100
12/12 [==============================] - 0s 6ms/step - loss: 0.9264 - accuracy: 0.5917 - val_loss: 0.8490 - val_accuracy: 0.5833
Epoch 8/100
12/12 [==============================] - 0s 5ms/step - loss: 0.8427 - accuracy: 0.5750 - val_loss: 0.7379 - val_accuracy: 0.7000
Epoch 9/100
12/12 [==============================] - 0s 5ms/step - loss: 0.7102 - accuracy: 0.7292 - val_loss: 0.6010 - val_accuracy: 0.8167
Epoch 10/100
12/12 [==============================] - 0s 4ms/step - loss: 0.5572 - accuracy: 0.7917 - val_loss: 0.4865 - val_accuracy: 0.8167
Epoch 11/100
12/12 [==============================] - 0s 5ms/step - loss: 0.5107 - accuracy: 0.8167 - val_loss: 0.4676 - val_accuracy: 0.8500
Epoch 12/100
12/12 [==============================] - 0s 4ms/step - loss: 0.3793 - accuracy: 0.8833 - val_loss: 0.4695 - val_accuracy: 0.7833
Epoch 13/100
12/12 [==============================] - 0s 4ms/step - loss: 0.3837 - accuracy: 0.8583 - val_loss: 0.3716 - val_accuracy: 0.8333
Epoch 14/100
12/12 [==============================] - 0s 4ms/step - loss: 0.3282 - accuracy: 0.8833 - val_loss: 0.3677 - val_accuracy: 0.8500
Epoch 15/100
12/12 [==============================] - 0s 5ms/step - loss: 0.2777 - accuracy: 0.9125 - val_loss: 0.3662 - val_accuracy: 0.8667
Epoch 16/100
12/12 [==============================] - 0s 4ms/step - loss: 0.2536 - accuracy: 0.9208 - val_loss: 0.3261 - val_accuracy: 0.8667
Epoch 17/100
12/12 [==============================] - 0s 3ms/step - loss: 0.2008 - accuracy: 0.9458 - val_loss: 0.3115 - val_accuracy: 0.8500
Epoch 18/100
12/12 [==============================] - 0s 6ms/step - loss: 0.1649 - accuracy: 0.9458 - val_loss: 0.3499 - val_accuracy: 0.8667
Epoch 19/100
12/12 [==============================] - 0s 5ms/step - loss: 0.1265 - accuracy: 0.9583 - val_loss: 0.3479 - val_accuracy: 0.8833
Epoch 20/100
12/12 [==============================] - 0s 4ms/step - loss: 0.1588 - accuracy: 0.9458 - val_loss: 0.3476 - val_accuracy: 0.8667
Epoch 21/100
12/12 [==============================] - 0s 3ms/step - loss: 0.1662 - accuracy: 0.9458 - val_loss: 0.3211 - val_accuracy: 0.8667
Epoch 22/100
12/12 [==============================] - 0s 3ms/step - loss: 0.1309 - accuracy: 0.9417 - val_loss: 0.3586 - val_accuracy: 0.8500
Epoch 23/100
12/12 [==============================] - 0s 3ms/step - loss: 0.0895 - accuracy: 0.9625 - val_loss: 0.3904 - val_accuracy: 0.8500
Epoch 24/100
12/12 [==============================] - 0s 3ms/step - loss: 0.1067 - accuracy: 0.9625 - val_loss: 0.5581 - val_accuracy: 0.8167
Epoch 25/100
12/12 [==============================] - 0s 6ms/step - loss: 0.1033 - accuracy: 0.9667 - val_loss: 0.3578 - val_accuracy: 0.9000
Epoch 26/100
12/12 [==============================] - 0s 4ms/step - loss: 0.0626 - accuracy: 0.9917 - val_loss: 0.3928 - val_accuracy: 0.8833
Epoch 27/100
12/12 [==============================] - 0s 3ms/step - loss: 0.0536 - accuracy: 0.9917 - val_loss: 0.3721 - val_accuracy: 0.9000
Epoch 28/100
12/12 [==============================] - 0s 4ms/step - loss: 0.0527 - accuracy: 0.9875 - val_loss: 0.4973 - val_accuracy: 0.8667
Epoch 29/100
12/12 [==============================] - 0s 3ms/step - loss: 0.0808 - accuracy: 0.9708 - val_loss: 0.4575 - val_accuracy: 0.8833
Epoch 30/100
12/12 [==============================] - 0s 3ms/step - loss: 0.1053 - accuracy: 0.9667 - val_loss: 0.4273 - val_accuracy: 0.8667
Epoch 31/100
12/12 [==============================] - 0s 5ms/step - loss: 0.0559 - accuracy: 0.9958 - val_loss: 0.4082 - val_accuracy: 0.9167
Epoch 32/100
12/12 [==============================] - 0s 3ms/step - loss: 0.0435 - accuracy: 0.9875 - val_loss: 0.5094 - val_accuracy: 0.8667
Epoch 33/100
12/12 [==============================] - 0s 3ms/step - loss: 0.0429 - accuracy: 0.9917 - val_loss: 0.4551 - val_accuracy: 0.9167
Epoch 34/100
12/12 [==============================] - 0s 3ms/step - loss: 0.0357 - accuracy: 0.9917 - val_loss: 0.5260 - val_accuracy: 0.8833
Epoch 35/100
12/12 [==============================] - 0s 3ms/step - loss: 0.0365 - accuracy: 0.9958 - val_loss: 0.4955 - val_accuracy: 0.9000
Epoch 36/100
12/12 [==============================] - 0s 3ms/step - loss: 0.0282 - accuracy: 1.0000 - val_loss: 0.3949 - val_accuracy: 0.9167
Epoch 37/100
12/12 [==============================] - 0s 3ms/step - loss: 0.0303 - accuracy: 0.9875 - val_loss: 0.4732 - val_accuracy: 0.9167
Epoch 38/100
12/12 [==============================] - 0s 3ms/step - loss: 0.0169 - accuracy: 1.0000 - val_loss: 0.4634 - val_accuracy: 0.9000
Epoch 39/100
12/12 [==============================] - 0s 3ms/step - loss: 0.0188 - accuracy: 0.9958 - val_loss: 0.4746 - val_accuracy: 0.9167
Epoch 40/100
12/12 [==============================] - 0s 3ms/step - loss: 0.0140 - accuracy: 0.9958 - val_loss: 0.5232 - val_accuracy: 0.8833
Epoch 41/100
12/12 [==============================] - 0s 3ms/step - loss: 0.0119 - accuracy: 0.9958 - val_loss: 0.4813 - val_accuracy: 0.9167
Epoch 42/100
12/12 [==============================] - 0s 3ms/step - loss: 0.0168 - accuracy: 0.9958 - val_loss: 0.4460 - val_accuracy: 0.9167
Epoch 43/100
12/12 [==============================] - 0s 3ms/step - loss: 0.0163 - accuracy: 0.9958 - val_loss: 0.4505 - val_accuracy: 0.9167
Epoch 44/100
12/12 [==============================] - 0s 3ms/step - loss: 0.0164 - accuracy: 0.9958 - val_loss: 0.4882 - val_accuracy: 0.9167
Epoch 45/100
12/12 [==============================] - 0s 3ms/step - loss: 0.0250 - accuracy: 0.9917 - val_loss: 0.5581 - val_accuracy: 0.9167
Epoch 46/100
12/12 [==============================] - 0s 3ms/step - loss: 0.0245 - accuracy: 0.9958 - val_loss: 0.4527 - val_accuracy: 0.9167
Epoch 47/100
12/12 [==============================] - 0s 3ms/step - loss: 0.0253 - accuracy: 0.9917 - val_loss: 0.4838 - val_accuracy: 0.9000
Epoch 48/100
12/12 [==============================] - 0s 3ms/step - loss: 0.0148 - accuracy: 0.9958 - val_loss: 0.4740 - val_accuracy: 0.9000
Epoch 49/100
12/12 [==============================] - 0s 3ms/step - loss: 0.0104 - accuracy: 1.0000 - val_loss: 0.4538 - val_accuracy: 0.9167
Epoch 50/100
12/12 [==============================] - 0s 3ms/step - loss: 0.0127 - accuracy: 0.9958 - val_loss: 0.5382 - val_accuracy: 0.9000
Epoch 51/100
12/12 [==============================] - 0s 3ms/step - loss: 0.0139 - accuracy: 0.9958 - val_loss: 0.4694 - val_accuracy: 0.9167
Epoch 52/100
12/12 [==============================] - 0s 3ms/step - loss: 0.0361 - accuracy: 0.9917 - val_loss: 0.4307 - val_accuracy: 0.9167
Epoch 53/100
12/12 [==============================] - 0s 3ms/step - loss: 0.0125 - accuracy: 0.9958 - val_loss: 0.5136 - val_accuracy: 0.9167
Epoch 54/100
12/12 [==============================] - 0s 3ms/step - loss: 0.0076 - accuracy: 1.0000 - val_loss: 0.4797 - val_accuracy: 0.9000
Epoch 55/100
12/12 [==============================] - 0s 3ms/step - loss: 0.0231 - accuracy: 0.9875 - val_loss: 0.5369 - val_accuracy: 0.9167
Epoch 56/100
12/12 [==============================] - 0s 3ms/step - loss: 0.0115 - accuracy: 1.0000 - val_loss: 0.5223 - val_accuracy: 0.9167
Epoch 57/100
12/12 [==============================] - 0s 3ms/step - loss: 0.0115 - accuracy: 0.9958 - val_loss: 0.6016 - val_accuracy: 0.9000
Epoch 58/100
12/12 [==============================] - 0s 3ms/step - loss: 0.0081 - accuracy: 1.0000 - val_loss: 0.7670 - val_accuracy: 0.8500
Epoch 59/100
12/12 [==============================] - 0s 3ms/step - loss: 0.0194 - accuracy: 0.9958 - val_loss: 0.5915 - val_accuracy: 0.9167
Epoch 60/100
12/12 [==============================] - 0s 3ms/step - loss: 0.0074 - accuracy: 1.0000 - val_loss: 0.5328 - val_accuracy: 0.9000
Epoch 61/100
12/12 [==============================] - 0s 3ms/step - loss: 0.0078 - accuracy: 1.0000 - val_loss: 0.5192 - val_accuracy: 0.9167
Epoch 62/100
12/12 [==============================] - 0s 3ms/step - loss: 0.0102 - accuracy: 0.9958 - val_loss: 0.5077 - val_accuracy: 0.9167
Epoch 63/100
12/12 [==============================] - 0s 3ms/step - loss: 0.0063 - accuracy: 1.0000 - val_loss: 0.5320 - val_accuracy: 0.9000
Epoch 64/100
12/12 [==============================] - 0s 3ms/step - loss: 0.0095 - accuracy: 0.9958 - val_loss: 0.6002 - val_accuracy: 0.8833
Epoch 65/100
12/12 [==============================] - 0s 3ms/step - loss: 0.0232 - accuracy: 0.9958 - val_loss: 1.0127 - val_accuracy: 0.8333
Epoch 66/100
12/12 [==============================] - 0s 3ms/step - loss: 0.0255 - accuracy: 0.9917 - val_loss: 0.5754 - val_accuracy: 0.9167
Epoch 67/100
12/12 [==============================] - 0s 3ms/step - loss: 0.0201 - accuracy: 0.9917 - val_loss: 0.5047 - val_accuracy: 0.8833
Epoch 68/100
12/12 [==============================] - 0s 3ms/step - loss: 0.0189 - accuracy: 0.9958 - val_loss: 0.6710 - val_accuracy: 0.8833
Epoch 69/100
12/12 [==============================] - 0s 3ms/step - loss: 0.0140 - accuracy: 1.0000 - val_loss: 0.5965 - val_accuracy: 0.9000
Epoch 70/100
12/12 [==============================] - 0s 3ms/step - loss: 0.0070 - accuracy: 1.0000 - val_loss: 0.5595 - val_accuracy: 0.9167
Epoch 71/100
12/12 [==============================] - 0s 3ms/step - loss: 0.0087 - accuracy: 1.0000 - val_loss: 0.5850 - val_accuracy: 0.8833
Epoch 72/100
12/12 [==============================] - 0s 3ms/step - loss: 0.0078 - accuracy: 1.0000 - val_loss: 0.5876 - val_accuracy: 0.9167
Epoch 73/100
12/12 [==============================] - 0s 3ms/step - loss: 0.0120 - accuracy: 0.9958 - val_loss: 0.6837 - val_accuracy: 0.9167
Epoch 74/100
12/12 [==============================] - 0s 3ms/step - loss: 0.0056 - accuracy: 1.0000 - val_loss: 0.6401 - val_accuracy: 0.9000
Epoch 75/100
12/12 [==============================] - 0s 3ms/step - loss: 0.0105 - accuracy: 0.9958 - val_loss: 0.6296 - val_accuracy: 0.9000
Epoch 76/100
12/12 [==============================] - 0s 3ms/step - loss: 0.0069 - accuracy: 1.0000 - val_loss: 0.5819 - val_accuracy: 0.9000
Epoch 77/100
12/12 [==============================] - 0s 3ms/step - loss: 0.0051 - accuracy: 1.0000 - val_loss: 0.5993 - val_accuracy: 0.9167
Epoch 78/100
12/12 [==============================] - 0s 3ms/step - loss: 0.0017 - accuracy: 1.0000 - val_loss: 0.6399 - val_accuracy: 0.9167
Epoch 79/100
12/12 [==============================] - 0s 3ms/step - loss: 0.0117 - accuracy: 0.9958 - val_loss: 0.7028 - val_accuracy: 0.9000
Epoch 80/100
12/12 [==============================] - 0s 3ms/step - loss: 0.0067 - accuracy: 1.0000 - val_loss: 0.6796 - val_accuracy: 0.8833
Epoch 81/100
12/12 [==============================] - 0s 3ms/step - loss: 0.0148 - accuracy: 0.9958 - val_loss: 0.8375 - val_accuracy: 0.8667
Epoch 82/100
12/12 [==============================] - 0s 3ms/step - loss: 0.0425 - accuracy: 0.9833 - val_loss: 1.1823 - val_accuracy: 0.8167
Epoch 83/100
12/12 [==============================] - 0s 3ms/step - loss: 0.0220 - accuracy: 0.9958 - val_loss: 0.7019 - val_accuracy: 0.9000
Epoch 84/100
12/12 [==============================] - 0s 3ms/step - loss: 0.0039 - accuracy: 1.0000 - val_loss: 0.8417 - val_accuracy: 0.8667
Epoch 85/100
12/12 [==============================] - 0s 3ms/step - loss: 0.0077 - accuracy: 1.0000 - val_loss: 0.7788 - val_accuracy: 0.8500
Epoch 86/100
12/12 [==============================] - 0s 3ms/step - loss: 0.0054 - accuracy: 1.0000 - val_loss: 0.7818 - val_accuracy: 0.9000
Epoch 87/100
12/12 [==============================] - 0s 3ms/step - loss: 0.0123 - accuracy: 0.9958 - val_loss: 0.7559 - val_accuracy: 0.9000
Epoch 88/100
12/12 [==============================] - 0s 3ms/step - loss: 0.0090 - accuracy: 0.9958 - val_loss: 0.7779 - val_accuracy: 0.9000
Epoch 89/100
12/12 [==============================] - 0s 3ms/step - loss: 0.0159 - accuracy: 0.9958 - val_loss: 0.9408 - val_accuracy: 0.8500
Epoch 90/100
12/12 [==============================] - 0s 3ms/step - loss: 0.0065 - accuracy: 1.0000 - val_loss: 0.7213 - val_accuracy: 0.9167
Epoch 91/100
12/12 [==============================] - 0s 3ms/step - loss: 0.0096 - accuracy: 1.0000 - val_loss: 0.6934 - val_accuracy: 0.9000
Epoch 92/100
12/12 [==============================] - 0s 3ms/step - loss: 0.0045 - accuracy: 1.0000 - val_loss: 0.7442 - val_accuracy: 0.9167
Epoch 93/100
12/12 [==============================] - 0s 3ms/step - loss: 0.0154 - accuracy: 0.9958 - val_loss: 0.7858 - val_accuracy: 0.9167
Epoch 94/100
12/12 [==============================] - 0s 3ms/step - loss: 0.0046 - accuracy: 1.0000 - val_loss: 0.8481 - val_accuracy: 0.9000
Epoch 95/100
12/12 [==============================] - 0s 3ms/step - loss: 0.0040 - accuracy: 1.0000 - val_loss: 0.7743 - val_accuracy: 0.9167
Epoch 96/100
12/12 [==============================] - 0s 3ms/step - loss: 0.0138 - accuracy: 0.9958 - val_loss: 0.7438 - val_accuracy: 0.9000
Epoch 97/100
12/12 [==============================] - 0s 3ms/step - loss: 0.0088 - accuracy: 1.0000 - val_loss: 0.7356 - val_accuracy: 0.8833
Epoch 98/100
12/12 [==============================] - 0s 5ms/step - loss: 0.0086 - accuracy: 0.9958 - val_loss: 0.8468 - val_accuracy: 0.9333
Epoch 99/100
12/12 [==============================] - 0s 3ms/step - loss: 0.0112 - accuracy: 0.9917 - val_loss: 0.8711 - val_accuracy: 0.9333
Epoch 100/100
12/12 [==============================] - 0s 3ms/step - loss: 0.0091 - accuracy: 0.9958 - val_loss: 0.8142 - val_accuracy: 0.9167

Result and Prediction

model.load_weights('best.hdf5')

model.evaluate(X_test,y_test, batch_size=20)

3/3 [==============================] - 0s 1ms/step - loss: 0.2314 - accuracy: 0.9833

[0.23137156665325165, 0.9833333492279053]

pred= model.predict(X_test)
predictions= np.argmax(pred, axis=1)
predictions

array([1, 0, 1, 1, 0, 0, 1, 2, 2, 2, 1, 0, 2, 1, 2, 1, 2, 2, 0, 2, 1, 0,
       1, 0, 0, 2, 1, 1, 2, 1, 1, 2, 2, 0, 0, 0, 1, 0, 1, 0, 0, 2, 1, 1,
       1, 1, 0, 1, 2, 0, 2, 2, 2, 1, 2, 1, 0, 2, 0, 2])

Accuracy and Loss Graph

plt.plot(history.history['accuracy'])
plt.plot(history.history['val_accuracy'])

[<matplotlib.lines.Line2D at 0x7f22400f99b0>]

plt.plot(history.history['loss'])
plt.plot(history.history['val_loss'])

[<matplotlib.lines.Line2D at 0x7f225abd79e8>]

Testing

plt.imshow(X_test[2])

<matplotlib.image.AxesImage at 0x7f21d07e2668>

y_train[2]

array([0., 1., 0.], dtype=float32)

predictions[2]

1

DeepCC

!deepCC best.h5

[INFO]
Reading [keras model] 'best.h5'
[SUCCESS]
Saved 'best_deepC/best.onnx'
[INFO]
Reading [onnx model] 'best_deepC/best.onnx'
[INFO]
Model info:
  ir_vesion : 5
  doc       : 
[WARNING]
[ONNX]: graph-node conv2d's attribute auto_pad has no meaningful data.
[WARNING]
[ONNX]: terminal (input/output) conv2d_input's shape is less than 1. Changing it to 1.
[WARNING]
[ONNX]: terminal (input/output) dense_1's shape is less than 1. Changing it to 1.
WARN (GRAPH): found operator node with the same name (dense_1) as io node.
[INFO]
Running DNNC graph sanity check ...
[SUCCESS]
Passed sanity check.
[INFO]
Writing C++ file 'best_deepC/best.cpp'
[INFO]
deepSea model files are ready in 'best_deepC/' 
[RUNNING COMMAND]
g++ -std=c++11 -O3 -fno-rtti -fno-exceptions -I. -I/opt/tljh/user/lib/python3.7/site-packages/deepC-0.13-py3.7-linux-x86_64.egg/deepC/include -isystem /opt/tljh/user/lib/python3.7/site-packages/deepC-0.13-py3.7-linux-x86_64.egg/deepC/packages/eigen-eigen-323c052e1731 "best_deepC/best.cpp" -D_AITS_MAIN -o "best_deepC/best.exe"
[RUNNING COMMAND]
size "best_deepC/best.exe"
   text	   data	    bss	    dec	    hex	filename
 690061	   3784	    760	 694605	  a994d	best_deepC/best.exe
[SUCCESS]
Saved model as executable "best_deepC/best.exe"