├── README.md
└── numeros.py


/README.md:
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1 | # youtube-tensorflow-mnist
2 | Código para red neuronal en python+tensorflow y set de datos MNIST
3 | 


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/numeros.py:
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  1 | from __future__ import absolute_import, division, print_function, unicode_literals
  2 | 
  3 | import tensorflow as tf
  4 | import tensorflow_datasets as tfds
  5 | 
  6 | import math
  7 | import numpy as np
  8 | import matplotlib.pyplot as plt
  9 | import logging
 10 | logger = tf.get_logger()
 11 | 
 12 | logger.setLevel(logging.ERROR)
 13 | 
 14 | 
 15 | dataset, metadata = tfds.load('mnist', as_supervised=True, with_info=True)
 16 | train_dataset, test_dataset = dataset['train'], dataset['test']
 17 | 
 18 | class_names = [
 19 |     'Cero', 'Uno', 'Dos', 'Tres', 'Cuatro', 'Cinco', 'Seis',
 20 |     'Siete', 'Ocho', 'Nueve'
 21 | ]
 22 | 
 23 | num_train_examples = metadata.splits['train'].num_examples
 24 | num_test_examples = metadata.splits['test'].num_examples
 25 | 
 26 | #Normalizar: Numeros de 0 a 255, que sean de 0 a 1
 27 | def normalize(images, labels):
 28 |     images = tf.cast(images, tf.float32)
 29 |     images /= 255
 30 |     return images, labels
 31 | 
 32 | train_dataset = train_dataset.map(normalize)
 33 | test_dataset = test_dataset.map(normalize)
 34 | 
 35 | #Estructura de la red
 36 | model = tf.keras.Sequential([
 37 | 	tf.keras.layers.Flatten(input_shape=(28,28,1)),
 38 | 	tf.keras.layers.Dense(64, activation=tf.nn.relu),
 39 | 	tf.keras.layers.Dense(64, activation=tf.nn.relu),
 40 | 	tf.keras.layers.Dense(10, activation=tf.nn.softmax) #para clasificacion
 41 | ])
 42 | 
 43 | #Indicar las funciones a utilizar
 44 | model.compile(
 45 | 	optimizer='adam',
 46 | 	loss='sparse_categorical_crossentropy',
 47 | 	metrics=['accuracy']
 48 | )
 49 | 
 50 | #Aprendizaje por lotes de 32 cada lote
 51 | BATCHSIZE = 32
 52 | train_dataset = train_dataset.repeat().shuffle(num_train_examples).batch(BATCHSIZE)
 53 | test_dataset = test_dataset.batch(BATCHSIZE)
 54 | 
 55 | #Realizar el aprendizaje
 56 | model.fit(
 57 | 	train_dataset, epochs=5,
 58 | 	steps_per_epoch=math.ceil(num_train_examples/BATCHSIZE) #No sera necesario pronto
 59 | )
 60 | 
 61 | #Evaluar nuestro modelo ya entrenado, contra el dataset de pruebas
 62 | test_loss, test_accuracy = model.evaluate(
 63 | 	test_dataset, steps=math.ceil(num_test_examples/32)
 64 | )
 65 | 
 66 | print("Resultado en las pruebas: ", test_accuracy)
 67 | 
 68 | 
 69 | for test_images, test_labels in test_dataset.take(1):
 70 | 	test_images = test_images.numpy()
 71 | 	test_labels = test_labels.numpy()
 72 | 	predictions = model.predict(test_images)
 73 | 
 74 | def plot_image(i, predictions_array, true_labels, images):
 75 | 	predictions_array, true_label, img = predictions_array[i], true_labels[i], images[i]
 76 | 	plt.grid(False)
 77 | 	plt.xticks([])
 78 | 	plt.yticks([])
 79 | 
 80 | 	plt.imshow(img[...,0], cmap=plt.cm.binary)
 81 | 
 82 | 	predicted_label = np.argmax(predictions_array)
 83 | 	if predicted_label == true_label:
 84 | 		color = 'blue'
 85 | 	else:
 86 | 		color = 'red'
 87 | 
 88 | 	plt.xlabel("Prediccion: {}".format(class_names[predicted_label]), color=color)
 89 | 
 90 | def plot_value_array(i, predictions_array, true_label):
 91 | 	predictions_array, true_label = predictions_array[i], true_label[i]
 92 | 	plt.grid(False)
 93 | 	plt.xticks([])
 94 | 	plt.yticks([])
 95 | 	thisplot = plt.bar(range(10), predictions_array, color="#888888")
 96 | 	plt.ylim([0,1])
 97 | 	predicted_label = np.argmax(predictions_array)
 98 | 
 99 | 	thisplot[predicted_label].set_color('red')
100 | 	thisplot[true_label].set_color('blue')
101 | 
102 | numrows=5
103 | numcols=3
104 | numimages = numrows*numcols
105 | 
106 | plt.figure(figsize=(2*2*numcols, 2*numrows))
107 | for i in range(numimages):
108 | 	plt.subplot(numrows, 2*numcols, 2*i+1)
109 | 	plot_image(i, predictions, test_labels, test_images)
110 | 	plt.subplot(numrows, 2*numcols, 2*i+2)
111 | 	plot_value_array(i, predictions, test_labels)
112 | 
113 | plt.show()


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