def TwoHidenLayerSigmodAndSoftmaxAdaGradMtd(DataLength, NumOfIteration):
DATA_DIR = './data/fashion'
mnist = LoadData.LoadDta(DATA_DIR, one_hot=True, reshape=False)
X = tf.placeholder(tf.float32, [None, 28, 28, 1])
Y_ = tf.placeholder(tf.float32, [None, 10])
sess = tf.Session()
RandNum = random.uniform(0, 0.7)
#RandNum = 0.6124315340304713
#RandNum = 0
print('RandNum........')
print(RandNum)
step = 0.00000005
#step = 0.00001
#weights
L1 = 300
L2 = 100
L3 = 10
W1 = tf.Variable(tf.truncated_normal([784, L1], stddev=RandNum))
W2 = tf.Variable(tf.truncated_normal([L1, L2], stddev=RandNum))
W3 = tf.Variable(tf.truncated_normal([L2, L3], stddev=RandNum))
#bias
B1 = tf.Variable(tf.zeros([1, L1], tf.float32))
B2 = tf.Variable(tf.zeros([1, L2], tf.float32))
B3 = tf.Variable(tf.zeros([1, L3], tf.float32))
XX = tf.reshape(X, [-1, 784])
#Model
Y1 = tf.nn.sigmoid(tf.matmul(XX, W1) + B1)
Y2 = tf.nn.sigmoid(tf.matmul(Y1, W2) + B2)
#Y1 = tf.nn.softmax(tf.matmul(XX, W1) + B1)
#Y2 = tf.nn.softmax(tf.matmul(Y1, W2) + B2)
Ylogits = tf.matmul(Y2, W3) + B3
Y = tf.nn.softmax(Ylogits)
#Cross Entropy
#cross_entropy = -tf.reduce_mean(Y_ * tf.log(Y))
cross_entropy = tf.nn.softmax_cross_entropy_with_logits(logits=Ylogits,
labels=Y_)
cross_entropy = tf.reduce_mean(cross_entropy)
# # accuracy of the trained model, between 0 (worst) and 1 (best)
correct_prediction = tf.equal(tf.argmax(Y, 1), tf.argmax(Y_, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
# # training, learning rate = 0.005
lr = 0.0001 + tf.train.exponential_decay(0.03, step, 2000, 1 / math.e)
#train_step = tf.train.AdamOptimizer(lr).minimize(cross_entropy)
train_step = tf.train.AdagradOptimizer(lr).minimize(cross_entropy)
#cost = tf.reduce_mean(tf.math.square(Y_ - Y))
#cost = -tf.reduce_mean(Y_ * (Y))
#train_step = tf.train.GradientDescentOptimizer(0.0001).minimize(cost)
# init
init = tf.global_variables_initializer()
sess = tf.Session()
sess.run(init)
TrngCrossEntropy = []
TestCrossEntropy = []
Testacc_Array = []
acc_Array = []
print(
'Two HiddenLayer Sigmoid and softmax Adagrad Traing Started>...............................'
)
print('Two Iteretion Number **************')
for i in range(1, NumOfIteration):
# training on batches of 100 images with 100 labels
batch_X, batch_Y = mnist.train.next_batch(DataLength)
#print(i)
sess.run(train_step, feed_dict={X: batch_X, Y_: batch_Y})
c, y, y_, w, b, a = sess.run([cross_entropy, Y, Y_, W1, B1, accuracy],
feed_dict={
X: batch_X,
Y_: batch_Y
})
acc_Array.append(a)
TrngCrossEntropy.append(c)
#TestAttrGen,TestClasGen = GenerateData(DataLength)
ct, y, y_, w, b, at = sess.run(
[cross_entropy, Y, Y_, W1, B1, accuracy],
feed_dict={
X: mnist.test.images[1:1000, :, :],
Y_: mnist.test.labels[1:1000, :]
})
#c, y,y_,w, b,a = sess.run([cross_entropy,Y,Y_, W1, B1,accuracy], feed_dict={X: batch_X, Y_: batch_Y})
Testacc_Array.append(at)
TestCrossEntropy.append(ct)
if ((i % 1000) == 0):
print('Iteration ')
print(i)
print('Accuracy')
print(at)
print('Cross-Entropy')
print(ct)
print('Singe Layer Traing completed')
return acc_Array, Testacc_Array, TrngCrossEntropy, TestCrossEntropy
def NoHidenLayerMtd(DataLength, NumOfIteration):
step = 0.00005
DATA_DIR = './data/fashion'
mnist = LoadData.LoadDta(DATA_DIR, one_hot=True, reshape=False)
X = tf.placeholder(tf.float32, [None, 28, 28, 1])
Y_ = tf.placeholder(tf.float32, [None, 10])
sess = tf.Session()
#Random Number
Randnum = random.uniform(0, 0.5)
#Randnum = 0.04526982
#weights
#W = tf.Variable(tf.zeros([784,10],tf.float32))
W = tf.Variable(tf.truncated_normal([784, 10], stddev=Randnum))
#bias
B = tf.Variable(tf.zeros([1, 10], tf.float32))
XX = tf.reshape(X, [-1, 784])
#Model
Y = tf.nn.softmax(tf.matmul(XX, W) + B)
#Cross Entropy
cross_entropy = -tf.reduce_mean(Y_ * tf.log(Y))
# # accuracy of the trained model, between 0 (worst) and 1 (best)
correct_prediction = tf.equal(tf.argmax(Y, 1), tf.argmax(Y_, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
# # training, learning rate = 0.005
lr = 0.0005 + tf.train.exponential_decay(0.03, step, 2000, 1 / math.e)
train_step = tf.train.AdamOptimizer(lr).minimize(cross_entropy)
#train_step = tf.train.GradientDescentOptimizer(0.0005).minimize(cross_entropy)
#cost = tf.reduce_mean(tf.math.square(Y_ - Y))
#cost = -tf.reduce_mean(Y_ * (Y))
#train_step = tf.train.GradientDescentOptimizer(0.0001).minimize(cost)
# init
init = tf.global_variables_initializer()
sess = tf.Session()
sess.run(init)
TrngCrossEntropy = []
TestCrossEntropy = []
Testacc_Array = []
acc_Array = []
print('Singe Layer Traing Started>...............................')
print('First Iteretion Number **************')
for i in range(1, NumOfIteration):
# training on batches of 100 images with 100 labels
batch_X, batch_Y = mnist.train.next_batch(DataLength)
#print(i)
sess.run(train_step, feed_dict={X: batch_X, Y_: batch_Y})
c, y, y_, w, b, a = sess.run([cross_entropy, Y, Y_, W, B, accuracy],
feed_dict={
X: batch_X,
Y_: batch_Y
})
acc_Array.append(a)
TrngCrossEntropy.append(c)
#TestAttrGen,TestClasGen = GenerateData(DataLength)
#c, y,y_,w, b,a = sess.run([cross_entropy,Y,Y_, W, B,accuracy], feed_dict={X: mnist.test.images, Y_: mnist.test.labels})
c, y, y_, w, b, a = sess.run([cross_entropy, Y, Y_, W, B, accuracy],
feed_dict={
X: batch_X,
Y_: batch_Y
})
Testacc_Array.append(a)
TestCrossEntropy.append(c)
print('Singe Layer Traing completed')
#acc_Array.append(a)#
print('c')
print(c)
print('y_Round')
print(np.round(y, 2))
print('y_Ceil')
#print(np.round(y) +' '+ y_)
print(np.round(y))
print('y_')
print(y_)
print('w')
print(w)
print('b')
print(b)
print('a')
print(a)
return acc_Array, Testacc_Array, TrngCrossEntropy, TestCrossEntropy
def FiveHidenLayerReluMtd(DataLength, NumOfIteration):
DATA_DIR = './data/fashion'
mnist = LoadData.LoadDta(DATA_DIR, one_hot=True, reshape=False)
X = tf.placeholder(tf.float32, [None, 28, 28, 1])
Y_ = tf.placeholder(tf.float32, [None, 10])
sess = tf.Session()
#RandNum = random.uniform(0,0.5)
RandNum = 0.23197358164873916
print('RandNum........')
print(RandNum)
step = 0.00000005
#weights
L1 = 300
L2 = 100
L3 = 10
#L4 = 15
#L5 = 10
W1 = tf.Variable(tf.truncated_normal([784, L1], stddev=RandNum))
W2 = tf.Variable(tf.truncated_normal([L1, L2], stddev=RandNum))
W3 = tf.Variable(tf.truncated_normal([L2, L3], stddev=RandNum))
#W4 = tf.Variable(tf.truncated_normal([L3,L4],stddev=RandNum))
#W5= tf.Variable(tf.truncated_normal([L4,L5],stddev=RandNum))
#bias
B1 = tf.Variable(tf.zeros([1, L1], tf.float32))
B2 = tf.Variable(tf.zeros([1, L2], tf.float32))
B3 = tf.Variable(tf.zeros([1, L3], tf.float32))
#B4 = tf.Variable(tf.zeros([1,L4],tf.float32))
#B5 = tf.Variable(tf.zeros([1,L5],tf.float32))
XX = tf.reshape(X, [-1, 784])
#Model
Y1 = tf.nn.relu(tf.matmul(XX, W1) + B1)
Y2 = tf.nn.relu(tf.matmul(Y1, W2) + B2)
#Y3 = tf.nn.relu(tf.matmul(Y2, W3) + B3)
#Y4 = tf.nn.relu(tf.matmul(Y3, W4) + B4)
Ylogits = tf.matmul(Y2, W3) + B3
Y = tf.nn.softmax(Ylogits)
#Cross Entropy
#cross_entropy = -tf.reduce_mean(Y_ * tf.log(Y))
cross_entropy = tf.nn.softmax_cross_entropy_with_logits(logits=Ylogits,
labels=Y_)
cross_entropy = tf.reduce_mean(cross_entropy)
# # accuracy of the trained model, between 0 (worst) and 1 (best)
correct_prediction = tf.equal(tf.argmax(Y, 1), tf.argmax(Y_, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
# # training, learning rate = 0.005
lr = 0.000001 + tf.train.exponential_decay(0.03, step, 2000, 1 / math.e)
#train_step = tf.train.AdamOptimizer(lr).minimize(cross_entropy)
train_step = tf.train.GradientDescentOptimizer(lr).minimize(cross_entropy)
#cost = tf.reduce_mean(tf.math.square(Y_ - Y))
#cost = -tf.reduce_mean(Y_ * (Y))
#train_step = tf.train.GradientDescentOptimizer(0.0001).minimize(cost)
# init
init = tf.global_variables_initializer()
sess = tf.Session()
sess.run(init)
TrngCrossEntropy = []
TestCrossEntropy = []
Testacc_Array = []
acc_Array = []
print('Singe Layer Traing Started>...............................')
print('Third Iteretion Number **************')
for i in range(1, NumOfIteration):
# training on batches of 100 images with 100 labels
batch_X, batch_Y = mnist.train.next_batch(DataLength)
#print(i)
sess.run(train_step, feed_dict={X: batch_X, Y_: batch_Y})
c, y, y_, w, b, a = sess.run([cross_entropy, Y, Y_, W1, B1, accuracy],
feed_dict={
X: batch_X,
Y_: batch_Y
})
acc_Array.append(a)
TrngCrossEntropy.append(c)
#TestAttrGen,TestClasGen = GenerateData(DataLength)
#c, y,y_,w, b,a = sess.run([cross_entropy,Y,Y_, W, B1,accuracy], feed_dict={X: mnist.test.images, Y_: mnist.test.labels})
c, y, y_, w, b, a = sess.run([cross_entropy, Y, Y_, W1, B1, accuracy],
feed_dict={
X: batch_X,
Y_: batch_Y
})
Testacc_Array.append(a)
TestCrossEntropy.append(c)
print(' Layer Traing completed')
#acc_Array.append(a)#
print('c')
print(c)
print('y_Round')
print(np.round(y, 2))
print('y_Ceil')
#print(np.round(y) +' '+ y_)
print(np.round(y))
print('y_')
print(y_)
print('w')
print(w)
print('b')
print(b)
print('a')
print(a)
return acc_Array, Testacc_Array, TrngCrossEntropy, TestCrossEntropy
def TwoHidenLayerMtd(DataLength,NumOfIteration):
DATA_DIR = './data/fashion'
mnist = LoadData.LoadDta(DATA_DIR,one_hot=True, reshape=False)
X = tf.placeholder(tf.float32,[None,28,28,1])
Y_ = tf.placeholder(tf.float32,[None,10])
sess = tf.Session()
#weights
L1 = 300
L2 = 100
L3 =10
W1 = tf.Variable(tf.zeros([784,L1],tf.float32))
W2 = tf.Variable(tf.zeros([L1,L2],tf.float32))
W3 = tf.Variable(tf.zeros([L2,L3],tf.float32))
#bias
B1 = tf.Variable(tf.zeros([1,L1],tf.float32))
B2 = tf.Variable(tf.zeros([1,L2],tf.float32))
B3 = tf.Variable(tf.zeros([1,L3],tf.float32))
XX = tf.reshape(X, [-1, 784])
#Model
Y1 = tf.nn.sigmoid(tf.matmul(XX, W1) + B1)
Y2 = tf.nn.sigmoid(tf.matmul(Y1, W2) + B2)
Y = tf.nn.softmax(tf.matmul(Y2, W3) + B3)
#Cross Entropy
cross_entropy = -tf.reduce_mean(Y_ * tf.log(Y))
# # accuracy of the trained model, between 0 (worst) and 1 (best)
correct_prediction = tf.equal(tf.argmax(Y, 1), tf.argmax(Y_, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
# # training, learning rate = 0.005
train_step = tf.train.GradientDescentOptimizer(0.0005).minimize(cross_entropy)
#cost = tf.reduce_mean(tf.math.square(Y_ - Y))
#cost = -tf.reduce_mean(Y_ * (Y))
#train_step = tf.train.GradientDescentOptimizer(0.0001).minimize(cost)
# init
init = tf.global_variables_initializer()
sess = tf.Session()
sess.run(init)
TrngCrossEntropy = []
TestCrossEntropy =[]
Testacc_Array = []
acc_Array = []
print('Singe Layer Traing Started>...............................')
for i in range(1,NumOfIteration):
# training on batches of 100 images with 100 labels
batch_X, batch_Y = mnist.train.next_batch(DataLength)
print('Iteretion Number **************')
print(i)
sess.run(train_step,feed_dict = {X: batch_X,Y_: batch_Y})
c, y,y_,w, b,a = sess.run([cross_entropy,Y,Y_, W, B,accuracy], feed_dict={X: batch_X, Y_: batch_Y})
acc_Array.append(a)
TrngCrossEntropy.append(c)
#TestAttrGen,TestClasGen = GenerateData(DataLength)
#c, y,y_,w, b,a = sess.run([cross_entropy,Y,Y_, W, B,accuracy], feed_dict={X: mnist.test.images, Y_: mnist.test.labels})
c, y,y_,w, b,a = sess.run([cross_entropy,Y,Y_, W, B,accuracy], feed_dict={X: batch_X, Y_: batch_Y})
Testacc_Array.append(a)
TestCrossEntropy.append(c)
print('Singe Layer Traing completed')
#acc_Array.append(a)#
print('c')
print(c)
print('y_Round')
print(np.round(y,2))
print('y_Ceil')
#print(np.round(y) +' '+ y_)
print(np.round(y))
print('y_')
print(y_)
print('w')
print(w)
print('b')
print(b)
print('a')
print(a)
return acc_Array,Testacc_Array,TrngCrossEntropy,TestCrossEntropy
def NoHidenLayerMtd(DataLength, NumOfIteration):
DATA_DIR = './data/fashion'
mnist = LoadData.LoadDta(DATA_DIR, one_hot=True, reshape=False)
X = tf.placeholder(tf.float32, [None, 28, 28, 1])
Y_ = tf.placeholder(tf.float32, [None, 10])
sess = tf.Session()
#weights
W = tf.Variable(tf.zeros([784, 10], tf.float32))
#bias
B = tf.Variable(tf.zeros([1, 10], tf.float32))
XX = tf.reshape(X, [-1, 784])
#Model
Y = tf.nn.softmax(tf.matmul(XX, W) + B)
#Cross Entropy
cross_entropy = -tf.reduce_mean(Y_ * tf.log(Y))
# # accuracy of the trained model, between 0 (worst) and 1 (best)
correct_prediction = tf.equal(tf.argmax(Y, 1), tf.argmax(Y_, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
# # training, learning rate = 0.005
train_step = tf.train.GradientDescentOptimizer(0.005).minimize(
cross_entropy)
#cost = tf.reduce_mean(tf.math.square(Y_ - Y))
#cost = -tf.reduce_mean(Y_ * (Y))
#train_step = tf.train.GradientDescentOptimizer(0.0001).minimize(cost)
# Create a summary to monitor cost tensor
tf.summary.scalar("loss", cross_entropy)
# Create a summary to monitor accuracy tensor
tf.summary.scalar("accuracy", accuracy)
tf.summary.histogram("Weights", W)
tf.summary.histogram("Bios", B)
merged_summary = tf.summary.merge_all()
writer = tf.summary.FileWriter(
"E:\\Study_materials\\Semester-4\\Project\\SourceCode\\Mid_Sem_Code\\FashionMaster25022019_backup\\FashionMaster02_backup\\tmp\\mnist_demo\\3"
)
writer.add_graph(sess.graph)
# init
init = tf.global_variables_initializer()
sess = tf.Session()
sess.run(init)
TrngCrossEntropy = []
TestCrossEntropy = []
Testacc_Array = []
acc_Array = []
print('No Hidden Layer Traing Started>...............................')
print('First Iteretion Number **************')
for i in range(1, NumOfIteration):
# training on batches of 100 images with 100 labels
batch_X, batch_Y = mnist.train.next_batch(DataLength)
batchShapeX = batch_X.shape
batchShapeY = batch_Y.shape
_, s = sess.run([train_step, merged_summary],
feed_dict={
X: batch_X,
Y_: batch_Y
})
c, y, y_, w, b, a = sess.run([cross_entropy, Y, Y_, W, B, accuracy],
feed_dict={
X: batch_X,
Y_: batch_Y
})
acc_Array.append(a)
TrngCrossEntropy.append(c)
#TestAttrGen,TestClasGen = GenerateData(DataLength)
ct, y, y_, w, b, at = sess.run([cross_entropy, Y, Y_, W, B, accuracy],
feed_dict={
X: mnist.test.images[1:1000, :, :],
Y_: mnist.test.labels[1:1000, :]
})
#c, y,y_,w, b,a = sess.run([cross_entropy,Y,Y_, W, B,accuracy], feed_dict={X: batch_X, Y_: batch_Y})
Testacc_Array.append(at)
TestCrossEntropy.append(ct)
# s = sess.run(merged_summary,feed_dict = {X: batch_X,Y_: batch_Y})
writer.add_summary(s, i)
# if ((i % 1000) == 0):
# print('Iteration ')
# print(i)
# print('Accuracy')
# print(at)
# print('Cross-Entropy')
# print(ct)
print('No Hidden Layer Traing completed')
np.savetxt(
'E:\\Study_materials\\Semester-4\\Project\\FinalSemProgress\\PlotData\\W1.txt',
w)
np.savetxt(
'E:\\Study_materials\\Semester-4\\Project\\FinalSemProgress\\PlotData\\B1.txt',
b)
return acc_Array, Testacc_Array, TrngCrossEntropy, TestCrossEntropy
