Model training

In this section, we will train our model with the same convolution neural network architecture that we used in Chapter 6, Improving the Image Classifier with CNN. Let's get started:

1. We will start by importing the keras and sklearn libraries. We are importing Sequential, Conv2D, MaxPooling2D, Dense, Flatten, Dropouts, Adam, TensorBoard, and check_ouput from Keras. We have imported train_test_split from sklearn:

import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
from subprocess import check_output
from sklearn.model_selection import train_test_split

2. Next, we will build the following model:

cnn_model = tf.keras.Sequential()
cnn_model.add(tf.keras.layers.Conv2D(32,3, 3, input_shape = image_shape, activation='relu'))
cnn_model.add(tf.keras.layers.Conv2D(64, (3,3), activation='relu'))
cnn_model.add(tf.keras.layers.MaxPooling2D(pool_size = (2, 2)))
cnn_model.add(tf.keras.layers.Dropout(0.25))
cnn_model.add(tf.keras.layers...