deep-learning/digits_mnist_trial_with_convnets.py at master · hydroweaver/deep-learning · GitHub

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# -*- coding: utf-8 -*-
"""
Created on Wed Jan  2 17:04:12 2019

@author: Karan.Verma
"""

import os
from keras import models
from keras import layers
from keras.datasets import mnist
import numpy as np
import matplotlib.pyplot as plt
from keras.utils import to_categorical, plot_model
import operator

(train_x, train_y), (test_x, test_y) = mnist.load_data()

def shape(arr):
    x = np.reshape(arr, (len(arr), 28, 28, 1))
    return x

#epochs
e = 5

#prediction array
eval_arr = {}
pred_arr = {}


#label summary
label_list = {0: '0', 1: '1', 2:'2', 3:'3', 4:'4', 5:'5', 6:'6', 7:'7', 8:'8', 9:'9'}

#path
output_dir_model = r'C:\Users\karan.verma\.spyder-py3\deep-learning\models'

train_x_reshaped = shape(train_x).astype('float32')/255
train_y = to_categorical(train_y)
train_y = train_y.astype('float32')

test_x_reshaped = shape(test_x).astype('float32')/255
test_y = to_categorical(test_y)
test_y = test_y.astype('float32')

#take out validation data, 25% = 15,000 samples

partial_train_x = train_x_reshaped[:int(len(train_x_reshaped)*0.75)]
partial_train_y = train_y[:int(len(train_y)*0.75)]

val_x = train_x_reshaped[int(len(train_x_reshaped)*0.75):]
val_y = train_y[int(len(train_y)*0.75):]

#make model
model = models.Sequential()
model.add(layers.Conv2D(32, (3, 3), activation='relu', input_shape=(28, 28, 1)))
model.add(layers.MaxPool2D((2, 2)))
model.add(layers.Conv2D(64, (3, 3), activation='relu'))
model.add(layers.MaxPool2D((2, 2)))
model.add(layers.Conv2D(64, (3, 3), activation='relu'))
model.add(layers.Flatten())
model.add(layers.Dense(64, activation='relu'))
model.add(layers.Dense(10, activation = 'sigmoid'))

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

#model fit

history = model.fit(partial_train_x,
                    partial_train_y,
                    epochs = e,
                    batch_size=64,
                    validation_data=(val_x, val_y),
                    verbose = 1)

acc_list = history.history
fig, ax = plt.subplots(2,1, figsize=(20, 10))

plt.subplot(211)
plt.plot(np.arange(1, e+1), acc_list['loss'], label='Training Loss')
plt.plot(np.arange(1, e+1), acc_list['val_loss'], label='Validation Loss')
plt.title('Loss Graph')
plt.xlabel('Epochs')
plt.ylabel('Loss')
plt.legend()

plt.subplot(212)
plt.plot(np.arange(1, e+1), acc_list['acc'], label='Training Acc')
plt.plot(np.arange(1, e+1), acc_list['val_acc'], label='Validation Acc')
plt.title('Accuracy Graph')
plt.xlabel('Epochs')
plt.ylabel('Accuracy')
plt.legend()

#plt.tight_layout()
fig.savefig('{}/Model figure.png'.format(output_dir_model))
plt.clf()

eval_arr['Model'] = model.evaluate(test_x_reshaped, test_y)
pred_arr['Model']= np.argmax(model.predict(test_x_reshaped), axis=1)


#plot_model(model, to_file='{}/model.png'.format(output_dir_model)) # this is kinda useless ! :|

#get best model from eval_arr
best_model = {}

for key, val in eval_arr.items():
    best_model[key] = val[1]

print('The best model is: {}'.format(max(best_model.items(), key=operator.itemgetter(1))))

#should also consider the loss of the best model....? right? high acc ...very high loss...is this good??

#Building Prediction Pipeline
#('Model having 1 hidden layers & 128 units in each hidden layer and 512 units in the outer layer',0.8953) but it varies

prediction = model.predict(test_x_reshaped)

print('Random samples from the test data: ')
fig, ax = plt.subplots(2, 3, figsize=(20, 10))
for i in range(1, 7):
        c = np.random.choice(len(test_x_reshaped))
        plt.subplot(2,3,i)
        plt.imshow(test_x[c])
        plt.title('Original {} & Predicted {}'.format(label_list[np.argmax(test_y[c])], label_list[np.argmax(prediction[c])]))
        #plt.tight_layout()