data science

1. X_train original shape (60000, 28, 28)
y_train original shape (60000,)
X_test original shape (10000, 28, 28)
y_test original shape (10000,)
Text(0.5, 1.0, '5')
(5, array([0., 0., 0., 0., 0., 1., 0., 0., 0., 0.], dtype=float32))
In [1]: import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
from keras.datasets import mnist
from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation
from keras.utils import np_utils
np.random.seed(35)
In [2]: (X_train, y_train), (X_test, y_test) = mnist.load_data()
print("X_train original shape", X_train.shape)
print("y_train original shape", y_train.shape)
print("X_test original shape", X_test.shape)
print("y_test original shape", y_test.shape)
In [3]: plt.imshow(X_train[0], cmap='gray')
plt.title(y_train[0])
Out[3]:
In [4]: X_train = X_train.reshape(60000,784)
X_test = X_test.reshape(10000,784)
X_train = X_train.astype('float32')
X_test = X_test.astype('float32')
X_train/=255
X_test/=255
In [5]: number_of_classes = 10
Y_train = np_utils.to_categorical(y_train, number_of_classes)
Y_test = np_utils.to_categorical(y_test, number_of_classes)
y_train[0], Y_train[0]
Out[5]:

2. ((50000, 784), (10000, 784))
Epoch 1/5
391/391 [==============================] - 59s 34ms/step - loss: 0.5052 - accuracy:
0.8394 - val_loss: 0.1180 - val_accuracy: 0.9640
Epoch 2/5
391/391 [==============================] - 12s 30ms/step - loss: 0.1169 - accuracy:
0.9646 - val_loss: 0.1011 - val_accuracy: 0.9705
Epoch 3/5
391/391 [==============================] - 12s 30ms/step - loss: 0.0794 - accuracy:
0.9744 - val_loss: 0.0913 - val_accuracy: 0.9741
Epoch 4/5
391/391 [==============================] - 12s 30ms/step - loss: 0.0661 - accuracy:
0.9792 - val_loss: 0.0850 - val_accuracy: 0.9780
Epoch 5/5
391/391 [==============================] - 12s 30ms/step - loss: 0.0545 - accuracy:
0.9824 - val_loss: 0.0875 - val_accuracy: 0.9783
In [6]: #shuffle the training set
for _ in range(5):
indexes = np.random.permutation(len(X_train))
X_train = X_train[indexes]
Y_train = Y_train[indexes]
#set aside 10,000 for validation
val_images = X_train[:10000,:]
val_labels = Y_train[:10000,:]
# leave rest in training set
train_images = X_train[10000:,:]
train_labels = Y_train[10000:,:]
train_images.shape, val_images.shape
Out[6]:
In [7]: model = Sequential()
model.add(Dense(512, input_dim=784))
# An "activation" is just a non-linear function applied to the output
# of the layer above. Here, with a "rectified linear unit",
# we clamp all values below 0 to 0.
model.add(Activation('relu'))
# Dropout helps protect the model from memorizing or "overfitting" the training data
model.add(Dropout(0.2))
model.add(Dense(512))
model.add(Activation('relu'))
model.add(Dropout(0.2))
model.add(Dense(512))
model.add(Activation('relu'))
model.add(Dropout(0.2))
model.add(Dense(10))
# This special "softmax" activation among other things,
# ensures the output is a valid probaility distribution, that is
# that its values are all non-negative and sum to 1.
model.add(Activation('softmax'))
In [8]: model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'
In [9]: history = model.fit(train_images, train_labels, epochs=5, batch_size=128,
validation_data=(val_images, val_labels))
In [10]: train_loss = history.history['loss']

3. <Figure size 432x288 with 0 Axes>
val_loss = history.history['val_loss']
In [11]: epochs = range(1, len(history.history['loss']) + 1)
In [13]: plt.plot(epochs, train_loss, 'bo', label='Training loss')
plt.plot(epochs, val_loss, 'g', label='Validation loss')
plt.title('Training and Validation Losses')
plt.xlabel('Epochs')
plt.ylabel('Loss')
plt.legend()
plt.show()
plt.savefig('Results/6_1_lossplot.png')
In [14]: train_accuracy = history.history['accuracy']
val_accuracy = history.history['val_accuracy']
In [15]: epochs = range(1, len(history.history['accuracy']) + 1)
In [16]: plt.plot(epochs, train_accuracy, 'bo', label='Training Accuracy')
plt.plot(epochs, val_accuracy, 'g', label='Validation Accuracy')
plt.title('Training and Validation Accuracy')
plt.xlabel('Epochs')
plt.ylabel('Accuracy')
plt.legend()
plt.show()
plt.savefig('results/6_1_accuracyplot.png')

4. <Figure size 432x288 with 0 Axes>
313/313 [==============================] - 2s 5ms/step - loss: 0.0770 - accuracy: 0.
9792
Test accuracy: 0.979200005531311
Actual Predictions
0 7 7
1 2 2
2 1 1
3 0 0
4 4 4
... ... ...
9995 2 2
9996 3 3
9997 4 4
9998 5 5
9999 6 6
[10000 rows x 2 columns]
In [17]: score = model.evaluate(X_test, Y_test)
print()
print('Test accuracy: ', score[1])
In [18]: 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_1_predictions.csv', index=False)
print (pred_res)
In [19]: # save model
model.save('results/6_1_model.h5')
In [20]: #Metrics output
with open('results/6_1_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 [ ]: