Red neuronal convolucional (CNN) en Tensorflow

Se supone que el lector conoce los conceptos de Redes Neuronales y Redes Neuronales Convolucionales. Si está seguro de los temas, consulte Redes neuronales y Redes neuronales convolucionales .

Bloques de construcción de CNN:

Las Redes Neuronales Convolucionales se componen principalmente de tres tipos de capas:

import tensorflow as tf
  
conv_layer = tf.keras.layers.Conv2D(
    filters, kernel_size, strides=(1, 1), padding='valid',
    data_format=None, dilation_rate=(1, 1), groups=1, activation=None,
    use_bias=True, kernel_initializer='glorot_uniform',
    bias_initializer='zeros', kernel_regularizer=None,
    bias_regularizer=None, activity_regularizer=None, kernel_constraint=None,
    bias_constraint=None, **kwargs
)

import tensorflow as tf
  
max_pooling_layer = tf.keras.layers.MaxPool2D(
    pool_size=(2, 2), strides=None, padding='valid', data_format=None,
    **kwargs
)
  
avg_pooling_layer = tf.keras.layers.AveragePooling2D(
    pool_size=(2, 2), strides=None, padding='valid', data_format=None,
    **kwargs
)

import tensorflow as tf
  
fully_connected_layer = tf.keras.layers.Dense(
    units, activation=None, use_bias=True,
    kernel_initializer='glorot_uniform',
    bias_initializer='zeros', kernel_regularizer=None,
    bias_regularizer=None, activity_regularizer=None, kernel_constraint=None,
    bias_constraint=None, **kwargs
)

from tensorflow.keras.datasets import mnist
(X_train, y_train), (X_test, y_test) = mnist.load_data()
print(f'X_train shape: {X_train.shape}, X_test shape: {X_test.shape}')
  
# Plotting random images from dataset
  
# import matplotlib.pyplot as plt 
# import random
# plt.figure(figsize = (12,5))
# for i in range(8):
#   ind = random.randint(0, len(X_train))
#   plt.subplot(240+1+i)
#   plt.imshow(X_train[ind], cmap=True)

from tensorflow.keras.utils import to_categorical
    
# convert image datatype from integers to floats
X_train = X_train.astype('float32')
X_test = X_test.astype('float32')
    
# normalising piel values
X_train = X_train/255.0
X_test = X_test/255.0
    
# reshape images to add channel dimension
X_train = X_train.reshape(X_train.shape[0], X_train.shape[1], X_train.shape[2], 1)
X_test = X_test.reshape(X_test.shape[0], X_test.shape[1], X_test.shape[2], 1)
    
# One-hot encoding label 
y_train = to_categorical(y_train)
y_test = to_categorical(y_test)

from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Conv2D, MaxPooling2D, Dense, Flatten
  
model = Sequential()
  
# Layer 1
# Conv 1
model.add(Conv2D(filters=6, kernel_size=(5, 5), strides=1, activation = 'relu', input_shape = (32,32,1)))
# Pooling 1
model.add(MaxPooling2D(pool_size=(2, 2), strides = 2))
  
# Layer 2
# Conv 2
model.add(Conv2D(filters=16, kernel_size=(5, 5), strides=1, activation='relu'))
# Pooling 2
model.add(MaxPooling2D(pool_size = 2, strides = 2))
  
# Flatten
model.add(Flatten())
  
# Layer 3
# Fully connected layer 1
model.add(Dense(units=120, activation='relu'))
  
#Layer 4
#Fully connected layer 2
model.add(Dense(units=84, activation='relu'))
  
#Layer 5
#Output Layer
model.add(Dense(units=10, activation='softmax'))
  
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])

epochs = 50
batch_size = 512
history = model.fit(X_train, y_train, epochs=epochs, batch_size=batch_size, 
                    steps_per_epoch=X_train.shape[0]//batch_size, 
                    validation_data=(X_test, y_test), 
                    validation_steps=X_test.shape[0]//batch_size, verbose = 1)
  
_, acc = model.evaluate(X_test, y_test, verbose = 1)
print('%.3f' % (acc * 100.0))
  
plt.figure(figsize=(10,6))
plt.plot(history.history['accuracy'], color = 'blue', label = 'train')
plt.plot(history.history['val_accuracy'], color = 'red', label = 'val')
plt.legend()
plt.title('Accuracy')
plt.show()