main.py

import os
import cv2
import numpy as np
import pickle
import copy
from model import *
import matplotlib.pyplot as plt

# Label index to label name relation
fashion_mnist_labels = {
    0: 'T-shirt/top',
    1: 'Pants',
    2: 'Pullover',
    3: 'Dress',
    4: 'Coat',
    5: 'Sandal',
    6: 'Shirt',
    7: 'Sneaker',
    8: 'Bag',
    9: 'Ankle boot'
}

# Input image PreProcessing
image_data = cv2.imread('images/tshirt.png', cv2.IMREAD_GRAYSCALE)
image_data = cv2.resize(image_data, (28, 28))
image_data = 255 - image_data
image_data = (image_data.reshape(1, -1).astype(np.float32) - 127.5) / 127.5

model = Model.load('trainedmodel.model')

# Predict on the image
confidences = model.predict(image_data)

# Get prediction instead of confidence levels
predictions = model.output_layer_activation.predictions(confidences)

# Get label name from label index
prediction = fashion_mnist_labels[predictions[0]]

print(prediction)

# plt.imshow(image_data)
# plt.show()




# Create dataset
X, y, X_test, y_test = create_data_mnist('fashion_mnist_images')

# Shuffle the training dataset
keys = np.array(range(X.shape[0]))
np.random.shuffle(keys)
X = X[keys]
y = y[keys]

# Scale and reshape samples
X = (X.reshape(X.shape[0], -1).astype(np.float32) - 127.5) / 127.5
X_test = (X_test.reshape(X_test.shape[0], -1).astype(np.float32) -
             127.5) / 127.5

# Initialize the model
model = Model()


# Add layers
model.add(Layer_Dense(X.shape[1], 128))
model.add(Activation_ReLU())
model.add(Layer_Dense(128, 128))
model.add(Activation_ReLU())
model.add(Layer_Dense(128, 10))
model.add(Activation_Softmax())


model.set(
    loss=Loss_CategoricalCrossentropy(),
    optimizer=Optimizer_SGD(learning_rate=0.25),
    accuracy=Accuracy_Categorical()
)

# model.finalize()

# # Train the model
# model.train(X, y, validation_data=(X_test, y_test),
#             epochs=20, batch_size=128, print_every=100)
# model.save('trainedmodel.model')