# \x/x\x/x\x/x\x/ -- fully connected layer (relu) W2 [200, 100] B2[100]
# · · · · · · · Y2 [batch, 100]
# \x/x\x/x\x/ -- fully connected layer (relu) W3 [100, 60] B3[60]
# · · · · · Y3 [batch, 60]
# \x/x\x/ -- fully connected layer (relu) W4 [60, 30] B4[30]
# · · · Y4 [batch, 30]
# \x/ -- fully connected layer (softmax) W5 [30, 10] B5[10]
# · Y5 [batch, 10]
import tensorflow as tf
print("Tensorflow version " + tf.__version__)
tf.set_random_seed(0)
np.random.seed(0)
# Read the training / testing dataset and labels
xTrain, yTrain, xTest, yTest, yLabels = readDatabase()
# Network parameters
layer1Size = 200
layer2Size = 100
layer3Size = 60
layer4Size = 30
# Train hyper-parameters
learningRate = 0.003
decay = 0.00035
noOfEpochs = 10
batchSize = 100
# Program parameters
required=False,
help="show images (0 = False, 1 = True)")
args = vars(ap.parse_args())
verbose = args["verbose"]
if verbose is None:
verbose = False
else:
if verbose == '1':
verbose = True
else:
verbose = False
# Read the training / testing dataset and labels
xTrain, yTrain, xTest, yTest, yLabels = readDatabase(reshape=True)
# Network parameters
firstConvLayerDepth = 4
secondConvLayerDepth = 8
thirdConvLayerDepth = 12
numberOfNeurons = 200
# Training hyperparameters
learningRate = 0.001
noOfEpochs = 3
batchSize = 32
numberOfClasses = yTrain.shape[1]
featureSize = xTrain.shape[1]
import argparse
ap = argparse.ArgumentParser()
ap.add_argument("-v", "--verbose", required=False, help="show images")
args = vars(ap.parse_args())
verbose = args["verbose"]
if verbose is None:
verbose = False
else:
verbose = bool(verbose)
sns.set(style='white', context='notebook', palette='deep')
xTrain, yTrain, xTest, yTest, yLabels = readDatabase(reshape=True,
categoricalValues=False)
barValues = yTrain.value_counts()
print("\nNumber of training dataset: ")
print(xTrain.shape)
print("\nNumber of of images per label: ")
print(barValues)
print("\nNumber of test dataset: ")
print(xTest.shape)
if verbose:
g = sns.countplot(yTrain)
plt.show()
g = sns.countplot(yTest)
