trX = np.load(data_dir + "trX.npy")
trX = trX[:, np.newaxis, :, :]
trY = np.load(data_dir + "trY.npy")
trY = np.concatenate((np.logical_not(trY).astype(np.int64), trY),
axis=1)
teX = np.load(data_dir + "teX.npy")
teX = teX[:, np.newaxis, :, :]
teY = np.load(data_dir + "teY.npy")
teY = np.concatenate((np.logical_not(teY).astype(np.int64), teY),
axis=1)
shape_dict = {
"w1": (32, 1, 3, 3),
"w2": (64, 32, 3, 3),
"w3": (128, 64, 3, 3),
"w4": (128 * 15 * 11, 11625),
"wo": (11625, 2)
}
else:
print("Dataset must be mnist, cifar, or office.")
sys.exit(1)
cnn = ConvolutionalNeuralNetwork().initialize_dataset(
trX, trY, teX, teY, shape_dict)
cnn.create_model_functions().train_net(epochs=10,
batch_size=32,
verbose=False)
print("Accuracy on the {0} dataset: {1:0.02f}%".format(
dataset,
cnn.calc_accuracy(teX, teY) * 100))
"w3": (128, 64, 3, 3),
"w4": (128 * 3 * 3, 841),
"wo": (841, 10)
}
elif dataset == "office":
data_dir = "/data1/user_data/office_objects/"
trX = np.load(data_dir + "trX.npy")
trX = trX[:, np.newaxis, :, :]
trY = np.load(data_dir + "trY.npy")
trY = np.concatenate((np.logical_not(trY).astype(np.int64), trY), axis = 1)
teX = np.load(data_dir + "teX.npy")
teX = teX[:, np.newaxis, :, :]
teY = np.load(data_dir + "teY.npy")
teY = np.concatenate((np.logical_not(teY).astype(np.int64), teY), axis = 1)
shape_dict = {
"w1": (32, 1, 3, 3),
"w2": (64, 32, 3, 3),
"w3": (128, 64, 3, 3),
"w4": (128 * 15 * 11, 11625),
"wo": (11625, 2)
}
else:
print("Dataset must be mnist, cifar, or office.")
sys.exit(1)
cnn = ConvolutionalNeuralNetwork().initialize_dataset(trX, trY, teX, teY, shape_dict)
cnn.create_model_functions().train_net(epochs = 10, batch_size = 32, verbose = False)
print("Accuracy on the {0} dataset: {1:0.02f}%".format(dataset, cnn.calc_accuracy(teX, teY)*100))
