1. Model: "sequential_7"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
conv2d_13 (Conv2D) (None, 32, 32, 32) 896
_________________________________________________________________
conv2d_14 (Conv2D) (None, 30, 30, 32) 9248
_________________________________________________________________
max_pooling2d_12 (MaxPooling (None, 15, 15, 32) 0
_________________________________________________________________
dropout_15 (Dropout) (None, 15, 15, 32) 0
_________________________________________________________________
conv2d_15 (Conv2D) (None, 13, 13, 64) 18496
_________________________________________________________________
conv2d_16 (Conv2D) (None, 11, 11, 64) 36928
_________________________________________________________________
max_pooling2d_13 (MaxPooling (None, 5, 5, 64) 0
_________________________________________________________________
dropout_16 (Dropout) (None, 5, 5, 64) 0
_________________________________________________________________
conv2d_17 (Conv2D) (None, 3, 3, 128) 73856
_________________________________________________________________
conv2d_18 (Conv2D) (None, 1, 1, 128) 147584
_________________________________________________________________
In [29]: from keras.datasets import cifar10
from keras.utils import to_categorical
(x_train, y_train), (x_test, y_test) = cifar10.load_data()
# preprocessing
x_train = x_train.astype('float32') # for division
x_test = x_test.astype('float32')
x_train /= 255 # normalise
x_test /= 255
num_classes = 10
y_train = to_categorical(y_train, num_classes)
y_test = to_categorical(y_test, num_classes)
In [30]: from keras import models
from keras import layers
model = models.Sequential()
model.add(layers.Conv2D(32, (3, 3), padding='same', input_shape=x_train.shape[1:]))
model.add(layers.Conv2D(32, (3, 3), activation='relu'))
model.add(layers.MaxPooling2D(pool_size=(2, 2)))
model.add(layers.Dropout(0.25))
model.add(layers.Conv2D(64, (3, 3), activation='relu'))
model.add(layers.Conv2D(64, (3, 3), activation='relu'))
model.add(layers.MaxPooling2D(pool_size=(2, 2)))
model.add(layers.Dropout(0.25))
model.add(layers.Conv2D(128, (3, 3), activation='relu'))
model.add(layers.Conv2D(128, (3, 3), activation='relu'))
model.add(layers.MaxPooling2D(pool_size=(2, 2), padding='same'))
model.add(layers.Dropout(0.25))
model.add(layers.Flatten())
model.add(layers.Dense(256, activation='relu'))
model.add(layers.Dropout(0.5))
model.add(layers.Dense(num_classes, activation='softmax'))
model.summary()

2. max_pooling2d_14 (MaxPooling (None, 1, 1, 128) 0
_________________________________________________________________
dropout_17 (Dropout) (None, 1, 1, 128) 0
_________________________________________________________________
flatten_4 (Flatten) (None, 128) 0
_________________________________________________________________
dense_7 (Dense) (None, 256) 33024
_________________________________________________________________
dropout_18 (Dropout) (None, 256) 0
_________________________________________________________________
dense_8 (Dense) (None, 10) 2570
=================================================================
Total params: 322,602
Trainable params: 322,602
Non-trainable params: 0
_________________________________________________________________
Epoch 1/10
1563/1563 [==============================] - 43s 27ms/step - loss: 1.9481 - accurac
y: 0.2608 - val_loss: 1.2860 - val_accuracy: 0.5321
Epoch 2/10
1563/1563 [==============================] - 40s 26ms/step - loss: 1.3612 - accurac
y: 0.5119 - val_loss: 1.1348 - val_accuracy: 0.5983
Epoch 3/10
1563/1563 [==============================] - 40s 26ms/step - loss: 1.1932 - accurac
y: 0.5855 - val_loss: 1.0013 - val_accuracy: 0.6464
Epoch 4/10
1563/1563 [==============================] - 41s 26ms/step - loss: 1.0883 - accurac
y: 0.6203 - val_loss: 0.9499 - val_accuracy: 0.6663
Epoch 5/10
1563/1563 [==============================] - 39s 25ms/step - loss: 1.0002 - accurac
y: 0.6536 - val_loss: 0.9631 - val_accuracy: 0.6649
Epoch 6/10
1563/1563 [==============================] - 40s 25ms/step - loss: 0.9573 - accurac
y: 0.6677 - val_loss: 0.8771 - val_accuracy: 0.6908
Epoch 7/10
1563/1563 [==============================] - 40s 26ms/step - loss: 0.8971 - accurac
y: 0.6904 - val_loss: 0.9248 - val_accuracy: 0.6825
Epoch 8/10
1563/1563 [==============================] - 39s 25ms/step - loss: 0.8803 - accurac
y: 0.6988 - val_loss: 0.8633 - val_accuracy: 0.6991
Epoch 9/10
1563/1563 [==============================] - 39s 25ms/step - loss: 0.8586 - accurac
y: 0.7057 - val_loss: 0.8159 - val_accuracy: 0.7234
Epoch 10/10
1563/1563 [==============================] - 39s 25ms/step - loss: 0.8308 - accurac
y: 0.7143 - val_loss: 0.8188 - val_accuracy: 0.7245
In [31]: model.compile(loss='categorical_crossentropy',
optimizer='adam',
metrics=['accuracy'])
In [32]: history=model.fit(x_train, y_train, batch_size=32,
validation_data=(x_test, y_test),
epochs=10)
In [33]: train_loss = history.history['loss']
val_loss = history.history['val_loss']
epochs = range(1, len(history.history['loss']) + 1)
In [35]: import matplotlib.pyplot as plt
plt.plot(epochs, train_loss, 'bo', label='Loss_Training')
plt.plot(epochs, val_loss, 'b', label='Loss_Validation')
plt.title('Training and Validation Losses')
plt.xlabel('Epochs')
plt.ylabel('Loss')

3. <Figure size 432x288 with 0 Axes>
<Figure size 432x288 with 0 Axes>
plt.legend()
plt.show()
plt.savefig('Results/6_2b_lossplot.png')
In [36]: train_loss = history.history['accuracy']
val_loss = history.history['val_accuracy']
epochs = range(1, len(history.history['accuracy']) + 1)
In [37]: plt.plot(epochs, train_loss, 'bo', label='Training accuracy')
plt.plot(epochs, val_loss, 'b', label='Validation accuracy')
plt.title('Training and Validation Accuracy')
plt.xlabel('Epochs')
plt.ylabel('Accuracy')
plt.legend()
plt.show()
plt.savefig('Results/6_2b_accplot.png')
In [38]: model.save('Results/6_2b_model.h5')
In [39]: score = model.evaluate(x_test, y_test)
print('Test accuracy: ', score[1])

4. 313/313 [==============================] - 2s 7ms/step - loss: 0.8188 - accuracy: 0.
7245
Test accuracy: 0.7245000004768372
Actual Predictions
0 [0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... 3
1 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, ... 8
2 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, ... 8
3 [1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... 0
4 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, ... 6
... ... ...
9995 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, ... 3
9996 [0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... 6
9997 [0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, ... 5
9998 [0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... 1
9999 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, ... 7
[10000 rows x 2 columns]
In [41]: import pandas as pd
import numpy as np
predictions = np.argmax(model.predict(x_test), axis=1)
predictions = list(predictions)
actuals = list(y_test)
pred_res = pd.DataFrame({'Actual': actuals, 'Predictions': predictions})
pred_res.to_csv('Results/6_2b_predictions.csv', index=False)
print (pred_res)
In [42]: #Metrics output
with open('Results/6_2b_metrics.txt', 'w') as f:
f.write('Training Loss: {}'.format(str(history.history['loss'])))
f.write('nTraining Accuracy: {}'.format(str(history.history['accuracy'])))
f.write('nTest Loss: {}'.format(score[0]))
f.write('nTest Accuracy: {}'.format(score[1]))
In [ ]: