import numpy

import pygad.cnn

train_inputs = numpy.load("dataset_inputs.npy")

train_outputs = numpy.load("dataset_outputs.npy")

sample_shape = train_inputs.shape[1:]

num_classes = 4

input_layer = pygad.cnn.Input2D(input_shape=sample_shape)

conv_layer1 = pygad.cnn.Conv2D(num_filters=2,

kernel_size=3,

previous_layer=input_layer,

activation_function=None)

relu_layer1 = pygad.cnn.Sigmoid(previous_layer=conv_layer1)

average_pooling_layer = pygad.cnn.AveragePooling2D(pool_size=2,

previous_layer=relu_layer1,

stride=2)

conv_layer2 = pygad.cnn.Conv2D(num_filters=3,

kernel_size=3,

previous_layer=average_pooling_layer,

activation_function=None)

relu_layer2 = pygad.cnn.ReLU(previous_layer=conv_layer2)

max_pooling_layer = pygad.cnn.MaxPooling2D(pool_size=2,

previous_layer=relu_layer2,

stride=2)

conv_layer3 = pygad.cnn.Conv2D(num_filters=1,

kernel_size=3,

previous_layer=max_pooling_layer,

activation_function=None)

relu_layer3 = pygad.cnn.ReLU(previous_layer=conv_layer3)

pooling_layer = pygad.cnn.AveragePooling2D(pool_size=2,

previous_layer=relu_layer3,

stride=2)

flatten_layer = pygad.cnn.Flatten(previous_layer=pooling_layer)

dense_layer1 = pygad.cnn.Dense(num_neurons=100,

previous_layer=flatten_layer,

activation_function="relu")

dense_layer2 = pygad.cnn.Dense(num_neurons=num_classes,

previous_layer=dense_layer1,

activation_function="softmax")

model = pygad.cnn.Model(last_layer=dense_layer2,

epochs=1,

learning_rate=0.01)

model.summary()

model.train(train_inputs=train_inputs,

train_outputs=train_outputs)

predictions = model.predict(data_inputs=train_inputs)

print(predictions)

num_wrong = numpy.where(predictions != train_outputs)[0]

num_correct = train_outputs.size - num_wrong.size

accuracy = 100 * (num_correct/train_outputs.size)

print("Number of correct classifications : {num_correct}.".format(num_correct=num_correct))

print("Number of wrong classifications : {num_wrong}.".format(num_wrong=num_wrong.size))

print("Classification accuracy : {accuracy}.".format(accuracy=accuracy))