def ff_labels_softmax():
X_train, y_train, X_val, y_val, X_test, y_test = load_dataset(
) #loads the mnist dataset
#X_train=np.concatenate((X_train,X_val,X_test),axis=0)[:65000,:]
#y_train=np.concatenate((y_train,y_val,y_test),axis=0)[:65000]
input_var = T.tensor4('inputs')
target_var = T.ivector('targets')
print("Building model and compiling functions...")
network = build_cnn(input_var)
test_prediction = lasagne.layers.get_output(network, deterministic=True)
ff_fn = theano.function([input_var],
test_prediction) #feed forward function
s = np.empty(
[10, 50000]
) #stores the indices of samples with decreasing probabilities(p1,p2,p3...,p10)
avg = np.empty([10]) #stores the average of store
no_of_clusters = 10
with np.load('model_dropout_test.npz') as f:
param_values = [f['arr_%d' % i] for i in range(len(f.files))]
lasagne.layers.set_all_param_values(network, param_values)
train_input = X_train
test_prediction = ff_fn(train_input)
ff_output = np.empty([50000])
for i in range(50000):
ff_output[i] = np.argmax(test_prediction[i, :])
'''
for i in range(10):
for n in range(no_of_clusters):
s[n,:]=np.argsort(test_prediction[:,n])[::-1]
avg[n]=np.mean(test_prediction[np.asarray(s[n,0:5000],int),n])
arg=np.argmax(avg)
print(arg)
for j in range(5000):
ff_output[s[arg,j]]=arg
test_prediction[:,arg]=0#equivalent to deleting the prob class
'''
return ff_output
import mytools
import networks
import main_methods
import load_mnist
theano.config.optimizer = "None"
params = mytools.get_CLI_parameters(sys.argv)
params["model"] = "mlp"
params["data"] = "mnist"
params["n_classes"] = 10
params["network_input_shape"] = (-1, 28 * 28)
params["epochs"] = 3
heatmap_shape = (28, 28)
X_train, y_train, X_val, y_val, X_test, y_test = load_mnist.load_dataset()
mean = np.mean(X_train)
std = np.std(X_train)
X_train = (X_train - mean) / std
X_val = (X_val - mean) / std
X_test = (X_test - mean) / std
data = (X_train, y_train,
X_val, y_val,
X_test, y_test)
# MLP TRAINING
mlp, mlp_params = main_methods.train_network(data, params)
mlp_prediction_func = mlp_params["prediction_func"]
if hasattr(mlp, "nonlinearity") and mlp.nonlinearity == softmax:
mlp.nonlinearity = lambda x: x
output_var_mlp = lasagne.layers.get_output(mlp)
#profmode = theano.ProfileMode(optimizer='fast_run', linker=theano.gof.OpWiseCLinker())
#profmode1 = theano.ProfileMode(optimizer='fast_run', linker=theano.gof.OpWiseCLinker())
def iterate_minibatches(inputs, targets, batchsize, shuffle=False):
assert len(inputs) == len(targets)
if shuffle:
indices = np.arange(len(inputs))
np.random.shuffle(indices)
for start_idx in range(0, len(inputs) - batchsize + 1, batchsize):
if shuffle:
excerpt = indices[start_idx:start_idx + batchsize]
else:
excerpt = slice(start_idx, start_idx + batchsize)
yield inputs[excerpt], targets[excerpt]
X_train, y_train, X_val, y_val, X_test, y_test = load_dataset(
) #loads the mnist dataset
#X_train=np.concatenate((X_train,X_val,X_test),axis=0)[:65000,:]
#y_train=np.concatenate((y_train,y_val,y_test),axis=0)[:65000]
input_var = T.tensor4('inputs')
target_var = T.fmatrix('targets')
print("Building model and compiling functions...")
network = build_cnn(input_var)
prediction = lasagne.layers.get_output(network)
loss = lasagne.objectives.binary_crossentropy(prediction, target_var)
lambd = 10
pred = prediction.sum(axis=0)
pred = pred / pred.sum()
pred1 = pred * T.log(pred)
loss = loss.mean() + lambd * pred1.mean()
def main():
trainx, trainy, testx, testy, valx, valy = load_dataset()
print(np.shape(trainx))