i am doing face recognition with convolution neural network here is my code
def load_dataset():
x_train=np.loadtxt('c:/database/gray_train.txt')
#x_train=train.read()
y_train=np.loadtxt('c:/database/y_train.txt')
x_val=np.loadtxt('c:/database/gray_test.txt')
y_val=np.loadtxt('c:/database/y_test.txt')
return x_train,y_train,x_val,y_val
def build_cnn(input_var=None):
l_in=L.InputLayer(shape=((None,3364)),input_var=input_var) #batch size is 100
l_shape=L.ReshapeLayer(l_in,(-1,1,58,58))
l_conv=L.Conv2DLayer(l_shape,num_filters=16,filter_size=(5,5),stride=(1,1),pad=0,nonlinearity=l.nonlinearities.rectify)
pool1=L.Pool2DLayer(l_conv,2)
l_conv2=L.Conv2DLayer(pool1,num_filters=32,filter_size=(4,4),stride=(1,1),pad=0,nonlinearity=l.nonlinearities.rectify)
pool2=L.Pool2DLayer(l_conv2,2)
l_conv3=L.Conv2DLayer(pool2,num_filters=48,filter_size=(3,3),stride=(2,2),pad=0,nonlinearity=l.nonlinearities.rectify)
pool3=L.Pool2DLayer(l_conv3,2)
fc1=L.DenseLayer(pool3,num_units=128)
l_output = L.DenseLayer(fc1,
num_units=16,nonlinearity=l.nonlinearities.softmax)
return l_output
def batch_gen(X,y,N):
while True:
idx=np.random.choice(len(y),N)
yield X[idx].astype('float32'),y[idx].astype('int32')
x_train,y_train,x_val,y_val=load_dataset()
x_sym=T.matrix()
y_sym=T.ivector()
l_out=build_cnn(x_sym)
output=L.get_output(l_out,x_sym)
pred=output.argmax(-1)
loss=T.mean(l.objectives.squared_error(output,y_sym))
acc=T.mean(T.eq(pred,y_sym))
params=L.get_all_params(l_out,Trainable=True)
grad=T.grad(loss,params)
updates=l.updates.sgd(grad,params,learning_rate=0.001)
f_train=t.function([x_sym,y_sym],[loss,acc],updates=updates)
f_val=t.function([x_sym,y_sym],[loss,acc])
f_predict=t.function([x_sym],pred)
batch_size=100
regression=True
max_epoch=10
verbose=1
n_batches=len(x_train)//batch_size
n_val_batches=len(x_val)//batch_size
train_batches=batch_gen(x_train,y_train,batch_size)
val_batches=batch_gen(x_val,y_val,batch_size)
for epoch in range(max_epoch):
train_loss=0
train_acc=0
for _ in range(n_batches):
x,y=next(train_batches)
#print(y)
loss,acc=f_train(x,y)
train_loss+=loss
train_acc+=acc
train_loss/=n_batches
train_acc/=n_batches
val_loss=0
val_acc=0
for _ in range(n_val_batches):
x,y=next(val_batches)
loss,acc=f_val(x,y)
val_loss/=n_val_batches
val_acc/=n_val_batches
print(epoch,train_loss,train_acc,val_loss/train_loss)
and i am getting error in loss,acc=f_train(x,y) error is x_sym=T.matrix()
Wrong number of dimensions: expected 2, got 1 with shape (100,). how
to resolve this issue i did resize x after x,y=next(train_batches) but
then i got an other error that says (100,1) can't be reshaped in [-1
58 58] in reshape layer
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