def validate():
conv_layer_1 = CNN.CNNLayer(3, 3, 1, 32)
conv_layer_2 = CNN.CNNLayer(32, 5, 2, 64)
pooling_layer_1 = CNN.PoolingLayer(2,2)
conv_layers = [conv_layer_1, conv_layer_2, pooling_layer_1]
ann = ANN.ANN(2304, [128, 3])
# 0 = Airplane
# 2 = Bird
# 8 = Ship
conv_layer_1.load("data/3-network/CNNL1.npz")
conv_layer_2.load("data/3-network/CNNL2.npz")
ann.load("data/3-network/FCL.npz")
batch = data_batch.DataBatch("cifar-10-python/cifar-10-batches-py/test_batch")
filteredImages = []
filteredLabels = []
for x in range(len(batch.labels)):
if(batch.labels[x] == 0):
filteredImages.append(batch.images[x])
filteredLabels.append(0)
if(batch.labels[x] == 2):
filteredImages.append(batch.images[x])
filteredLabels.append(1)
if(batch.labels[x] == 8):
filteredImages.append(batch.images[x])
filteredLabels.append(2)
batch.images = np.array(filteredImages)
batch.labels = np.array(filteredLabels)
data = batch.images
for i in range(len(conv_layers)):
data = conv_layers[i].forward(data)
flattened = data.reshape(data.shape[0], data.shape[1] * data.shape[2] * data.shape[3])
output = ann.prop_forward(flattened)
right = 0
seen = 0
for i in range(batch.images.shape[0]):
outputNum = -1
biggest = -1
for j in range(0, 3):
if output[i][j] > biggest:
outputNum = j
biggest = output[i][j]
seen+=1
if outputNum == batch.labels[i]:
right += 1
print("Right: " + str(right) + " / " + str(seen) + " - " + "{0:.0%}".format(right/seen))
def train():
batch_files = ["cifar-10-python/cifar-10-batches-py/data_batch_1",
"cifar-10-python/cifar-10-batches-py/data_batch_2",
"cifar-10-python/cifar-10-batches-py/data_batch_3",
"cifar-10-python/cifar-10-batches-py/data_batch_4",
"cifar-10-python/cifar-10-batches-py/data_batch_5"]
conv_layer_1 = CNN.CNNLayer(3, 3, 1, 32)
conv_layer_2 = CNN.CNNLayer(32, 5, 2, 64)
pooling_layer_1 = CNN.PoolingLayer(2,2)
conv_layers = [conv_layer_1, conv_layer_2, pooling_layer_1]
ann = ANN.ANN(2304, [128, 3])
# 0 = Airplane
# 2 = Bird
# 8 = Ship
conv_layer_1.load("data/3-network/CNNL1.npz")
conv_layer_2.load("data/3-network/CNNL2.npz")
ann.load("data/3-network/FCL.npz")
for epoch in range(0, 40):
print ("epoch " + str(epoch))
batch_sizes = 64
for filename in batch_files:
batch = data_batch.DataBatch(filename)
filteredImages = []
filteredLabels = []
for x in range(len(batch.labels)):
if(batch.labels[x] == 0):
filteredImages.append(batch.images[x])
filteredLabels.append(0)
if(batch.labels[x] == 2):
filteredImages.append(batch.images[x])
filteredLabels.append(1)
if(batch.labels[x] == 8):
filteredImages.append(batch.images[x])
filteredLabels.append(2)
batch.images = np.array(filteredImages)
batch.labels = np.array(filteredLabels)
print("Running on file " + filename[-12:])
order = list(range(0, len(batch.images), batch_sizes))
random.shuffle(order)
for i in order:
conv_layers, ann = trainOn(batch.images[i:i+batch_sizes], batch.labels[i:i+batch_sizes],
conv_layers, ann, True, 0.000004 * math.pow(0.96, epoch), 0.7) #Decay and momentum
print("saving")
conv_layer_1.save("data/3-network/CNNL1.npz")
conv_layer_2.save("data/3-network/CNNL2.npz")
ann.save("data/3-network/FCL.npz")
def __init__(self):
conv_layer_1 = CNN.CNNLayer(3, 3, 1, 32)
conv_layer_2 = CNN.CNNLayer(32, 5, 2, 64)
pooling_layer_1 = CNN.PoolingLayer(2, 2)
self.conv_layers = [conv_layer_1, conv_layer_2, pooling_layer_1]
self.ann = ANN.ANN(2304, [128, 3])
# 0 = Airplane
# 2 = Bird
# 8 = Ship
conv_layer_1.load("data/3-network/CNNL1.npz")
conv_layer_2.load("data/3-network/CNNL2.npz")
self.ann.load("data/3-network/FCL.npz")
