Introducción a la API Sequential de Keras
Contents
Introducción a la API Sequential de Keras#
Introducción#
Este es un notebook de Google Colaboratory. Los programas de Python se ejecutan directamente en tu navegador, una gran manera de aprender y utilizar TensorFlow. Para poder seguir este tutorial, ejecuta este notebook en Google Colab presionando el botón en la parte superior de esta pagina.
from __future__ import absolute_import, division, print_function
import tensorflow as tf
print('Version de Tensorflow = ', tf.__version__)
Version de Tensorflow = 2.9.1
Prepara datos de MNIST#
mnist = tf.keras.datasets.mnist
(x_train, y_train), (x_test, y_test) = mnist.load_data()
x_train, x_test = x_train/255.0, x_test/255.0
Modelo Sequential de Keras#
import tensorflow.keras.layers as layer
model = tf.keras.models.Sequential([
layer.Flatten(input_shape=(28,28)), #784
layer.Dense(128, activation='relu'),
layer.Dropout(0.2),
layer.Dense(10, activation='softmax')
])
model.compile(optimizer='adam',
loss='sparse_categorical_crossentropy',
metrics=['accuracy'])
Summary y Plot Model#
model.summary()
Model: "sequential"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
flatten (Flatten) (None, 784) 0
dense (Dense) (None, 128) 100480
dropout (Dropout) (None, 128) 0
dense_1 (Dense) (None, 10) 1290
=================================================================
Total params: 101,770
Trainable params: 101,770
Non-trainable params: 0
_________________________________________________________________
from tensorflow.keras.utils import plot_model
plot_model(model, to_file='../Imagenes/mnist_dense.png',
show_shapes=True, show_dtype=False)
You must install pydot (`pip install pydot`) and install graphviz (see instructions at https://graphviz.gitlab.io/download/) for plot_model/model_to_dot to work.
Puede necesitar instalar graphviz.
#from plot_model import plot_model
#plot_model(model)
Entrenamiento y evaluación del modelo#
history = model.fit(x_train, y_train, epochs=5)
Epoch 1/5
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Epoch 2/5
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Epoch 3/5
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Epoch 4/5
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Epoch 5/5
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495/1875 [======>.......................] - ETA: 4s - loss: 0.0745 - accuracy: 0.9763
513/1875 [=======>......................] - ETA: 4s - loss: 0.0749 - accuracy: 0.9762
531/1875 [=======>......................] - ETA: 4s - loss: 0.0750 - accuracy: 0.9764
548/1875 [=======>......................] - ETA: 4s - loss: 0.0753 - accuracy: 0.9763
568/1875 [========>.....................] - ETA: 4s - loss: 0.0753 - accuracy: 0.9763
588/1875 [========>.....................] - ETA: 4s - loss: 0.0748 - accuracy: 0.9762
607/1875 [========>.....................] - ETA: 4s - loss: 0.0752 - accuracy: 0.9761
625/1875 [=========>....................] - ETA: 4s - loss: 0.0751 - accuracy: 0.9761
645/1875 [=========>....................] - ETA: 3s - loss: 0.0753 - accuracy: 0.9762
665/1875 [=========>....................] - ETA: 3s - loss: 0.0751 - accuracy: 0.9761
model.evaluate(x_test, y_test, verbose=2)
313/313 - 2s - loss: 0.0738 - accuracy: 0.9786
[0.07382926344871521, 0.978600025177002]
Primeros gráficos#
Extrae datos#
import matplotlib.pyplot as plt
import pandas as pd
hist = pd.DataFrame(history.history)
hist['epoch'] = history.epoch
hist
| loss | accuracy | epoch | |
|---|---|---|---|
| 0 | 0.293235 | 0.916267 | 0 |
| 1 | 0.140098 | 0.958250 | 1 |
| 2 | 0.105634 | 0.968300 | 2 |
| 3 | 0.088549 | 0.972383 | 3 |
| 4 | 0.076502 | 0.976267 | 4 |
Pérdida#
plt.figure()
plt.xlabel('Epoca')
plt.ylabel('Loss: Entropía cruzada')
plt.plot(hist['epoch'], hist['loss'],
label='Error en entrenamiento')
plt.ylim([0,0.5])
plt.legend()
plt.show()
Exactitud#
plt.figure()
plt.xlabel('Epoca')
plt.ylabel('Exactitud')
plt.plot(hist['epoch'], hist['accuracy'],
label='Exactitud en entrenamiento')
plt.ylim([0.9,1])
plt.legend()
plt.show()
