def init_toy_model():
np.random.seed(0)
input_size = 4
hidden_size = 10
num_classes = 3
num_inputs = 5
return TwoLayerNet(input_size, hidden_size, num_classes, std=1e-1)
def getQoS(self):
X_test, y_test = self._get_test_data()
input_size = 32 * 32 * 3
hidden_size = 110
num_classes = 10
net = TwoLayerNet(input_size, hidden_size, num_classes)
try:
net.params['W1'] = pickle.load(open(self.run_dir + "model_nn_w1.p",
"rb"),
encoding='latin1')
net.params['b1'] = pickle.load(open(self.run_dir + "model_nn_b1.p",
"rb"),
encoding='latin1')
net.params['W2'] = pickle.load(open(self.run_dir + "model_nn_w2.p",
"rb"),
encoding='latin1')
net.params['b2'] = pickle.load(open(self.run_dir + "model_nn_b2.p",
"rb"),
encoding='latin1')
test_accuracy = (net.predict(X_test) == y_test).mean()
except:
test_accuracy = 0.0
print("qos", str(test_accuracy))
return test_accuracy * 100.0
def NeuralNet(train_data, train_label, validation_data, validation_label,
test_data, test_label):
input_size = 32 * 32 * 3
hidden_size = 50
num_classes = 10
net = TwoLayerNet(input_size, hidden_size, num_classes)
print train_label.shape
# Train the network
stats = net.train(train_data,
train_label,
validation_data,
validation_label,
num_iters=4000,
batch_size=1500,
learning_rate=5e-3,
learning_rate_decay=0.96,
reg=2,
verbose=True,
method='adam')
# Predict on the validation set
val_acc = (net.predict(validation_data) == validation_label).mean()
print('Validation accuracy: ', val_acc)
# Plot the loss function and train / validation accuracies
plt.subplot(2, 1, 1)
plt.plot(stats['loss_history'])
plt.title('Loss history')
plt.xlabel('Iteration')
plt.ylabel('Loss')
plt.subplot(2, 1, 2)
plt.plot(stats['train_acc_history'], label='train')
plt.plot(stats['val_acc_history'], label='val')
plt.title('Classification accuracy history')
plt.xlabel('Epoch')
plt.ylabel('Clasification accuracy')
plt.legend()
plt.show()
show_net_weights(net)
def main(job_id, params):
print('Training new neural net and calculating validation value job #%d' % job_id)
np.random.seed(0) # For repeatability.
X_train, y_train, X_val, y_val, _, _ = get_CIFAR10_data()
input_size = 32 * 32 * 3
num_classes = 10
hidden_size = params['hidden_size']
lr = params['learning_rate']
lrdc = params['learning_rate_decay']
r = params['regularization']
net = TwoLayerNet(input_size, hidden_size, num_classes)
stats = net.train(X_train, y_train, X_val, y_val,
num_iters=600, batch_size=200,
learning_rate=lr, learning_rate_decay=lrdc,
reg=r)
val_acc = (net.predict(X_val) == y_val).mean()
print('Validation accuracy: ', val_acc)
# We wish to maximize the validation accuracy, so minimize the negative.
return -val_acc
################################################################################
# TODO: Train a two-layer neural network on image features. You may want to #
# cross-validate various parameters as in previous sections. Store your best #
# model in the best_net variable. #
################################################################################
#pass
learning_rates = [1]
regularization_strengths = [0, 0.25, 0.5, 1]
learning_rate_decay = [1, 0.95, 0.8]
for _l in learning_rates:
for _r in regularization_strengths:
for _d in learning_rate_decay:
net = TwoLayerNet(input_dim, hidden_dim, num_classes)
# Train the network
stats = net.train(X_train_feats,
y_train,
X_val_feats,
y_val,
num_iters=1000,
batch_size=200,
learning_rate=_l,
learning_rate_decay=_d,
reg=_r,
verbose=True)
# Predict on the validation set
val_accuracy = (net.predict(X_val_feats) == y_val).mean()
print(
# plt.subplot(examples_per_class, len(classes), i * len(classes) + cls + 1)
# plt.imshow(X_test[idx].astype('uint8'))
# plt.axis('off')
# if i == 0:
# plt.title(cls_name)
#plt.show()
print X_train_feats.shape
from cs231n.classifiers.neural_net import TwoLayerNet
input_dim = X_train_feats.shape[1]
hidden_dim = 500
num_classes = 10
net = TwoLayerNet(input_dim, hidden_dim, num_classes)
best_net = None
################################################################################
# TODO: Train a two-layer neural network on image features. You may want to #
# cross-validate various parameters as in previous sections. Store your best #
# model in the best_net variable. #
################################################################################
best_val = -1
input_size = 32 * 32 * 3
hidden_size = 50
learning_rates = [1e-1, 1]
regularization_strengths = [1e-3, 5e-3]
results = {}
for rate in learning_rates:
for strength in regularization_strengths:
# Preprocessing: Remove the bias dimension
# Make sure to run this cell only ONCE
print(X_train_feats.shape)
X_train_feats = X_train_feats[:, :-1]
X_val_feats = X_val_feats[:, :-1]
X_test_feats = X_test_feats[:, :-1]
print(X_train_feats.shape)
from cs231n.classifiers.neural_net import TwoLayerNet
input_dim = X_train.shape[1]
hidden_dim = 600
num_classes = 10
net = TwoLayerNet(input_dim, hidden_dim, num_classes)
best_net = None
################################################################################
# TODO: Train a two-layer neural network on image features. You may want to #
# cross-validate various parameters as in previous sections. Store your best #
# model in the best_net variable. #
################################################################################
# Train the network
stats = net.train(X_train,
y_train,
X_val,
y_val,
num_iters=1000,
batch_size=200,
learning_rate=5e-3,
# # For completeness, we should also try training a neural network on image features. This approach should outperform all previous approaches: you should easily be able to achieve over 55% classification accuracy on the test set; our best model achieves about 60% classification accuracy. # In[ ]: print(X_train_feats.shape) # In[ ]: from cs231n.classifiers.neural_net import TwoLayerNet input_dim = X_train_feats.shape[1] hidden_dim = 500 num_classes = 10 net = TwoLayerNet(input_dim, hidden_dim, num_classes) best_net = None ################################################################################ # TODO: Train a two-layer neural network on image features. You may want to # # cross-validate various parameters as in previous sections. Store your best # # model in the best_net variable. # ################################################################################ pass ################################################################################ # END OF YOUR CODE # ################################################################################ # In[ ]: # Run your neural net classifier on the test set. You should be able to
i * len(classes) + cls + 1)
plt.imshow(X_test[idx].astype('uint8'))
plt.axis('off')
if i == 0:
plt.title(cls_name)
plt.show()
print X_train_feats.shape
from cs231n.classifiers.neural_net import TwoLayerNet
input_dim = X_train_feats.shape[1]
hidden_dim = 500
num_classes = 10
net = TwoLayerNet(input_dim, hidden_dim, num_classes)
best_net = None
################################################################################
# Train a two-layer neural network on image features. You may want to #
# cross-validate various parameters as in previous sections. Store your best #
# model in the best_net variable. #
################################################################################
results = {}
best_val = -1
best_static = None
learning_rates = [0.5, 1, 5]
regularization_strengths = [0.01, 0.001, 0.001]
num_iterations = 2000
for lr in learning_rates:
for reg in regularization_strengths:
num_classes = 10
best_net = None
best_val_acc = 0
results = {}
learning_rates = [0.3, 0.5, 0.6, 0.7, 0.8, 0.9, 1, 1.5]
hidden_sizes = [500]
regs = [1e-5, 1e-4, 1e-3, 1e-2]
num_iters = [1000, 2000, 5000]
for learnRate in learning_rates:
for hiddenSize in hidden_sizes:
for theReg in regs:
for numIter in num_iters:
net = TwoLayerNet(input_dim, hiddenSize, num_classes)
# Train the network
stats = net.train(X_train_feats,
y_train,
X_val_feats,
y_val,
num_iters=numIter,
batch_size=200,
learning_rate=learnRate,
learning_rate_decay=0.95,
reg=theReg,
verbose=True)
# Predict on the validation set
val_acc = (net.predict(X_val_feats) == y_val).mean()
hidden_sizes = [80, 90, 100]
learning_rates = [1.8e-3, 2e-3, 2.2e-3]
regularization_strengths = [0.5, 0.6, 0.7, 0.8]
batch_sizes = [256, 512, 1024, 2048]
hidden_sizes = [100]
learning_rates = [2e-3]
regularization_strengths = [0.5]
batch_sizes = [1024]
for learning_rate in learning_rates:
for reg in regularization_strengths:
for hidden_size in hidden_sizes:
for batch_size in batch_sizes:
net = TwoLayerNet(input_size, hidden_size, num_classes)
stats = net.train(X_train,
y_train,
X_val,
y_val,
num_iters=5000,
batch_size=batch_size,
learning_rate=learning_rate,
learning_rate_decay=0.95,
reg=reg,
verbose=True)
# training and validation set
training_accuracy = stats['train_acc_history'][-1]
print('training accuracy: %f' % (training_accuracy, ))
validation_accuracy = stats['val_acc_history'][-1]
print('validation accuracy: %f' % (validation_accuracy, ))
i * len(classes) + cls + 1)
plt.imshow(X_test[idx].astype('uint8'))
plt.axis('off')
if i == 0:
plt.title(cls_name)
plt.show()
print(X_train_feats.shape)
from cs231n.classifiers.neural_net import TwoLayerNet
input_dim = X_train_feats.shape[1]
hidden_dim = 500
num_classes = 10
net = TwoLayerNet(input_dim, hidden_dim, num_classes)
best_net = None
################################################################################
# TODO: Train a two-layer neural network on image features. You may want to #
# cross-validate various parameters as in previous sections. Store your best #
# model in the best_net variable. #
################################################################################
workers = 10
best_accuracy = 0
learning_rates = [0.9]
numer_of_training_epochs = [4000]
reg = [0.002286, 0.002287, 0.002288, 0.002289, 0.00229]
#learning_rates = [10 ** np.random.uniform(-4, -2) for _ in range(workers)]
#numer_of_training_epochs = [random.randint(4000,5000) for _ in range(workers)]
print "Train labels shape: ", y_train.shape
print "Validation data shape: ", X_val.shape
print "Validation labels shape: ", y_val.shape
print "Test data shape: ", X_test.shape
print "Test labels shape: ", y_test.shape
# # Train a network
# To train our network we will use SGD with momentum. In addition, we will adjust the learning rate with an exponential learning rate schedule as optimization proceeds; after each epoch, we will reduce the learning rate by multiplying it by a decay rate.
# In[ ]:
input_size = 32 * 32 * 3
hidden_size = 50
num_classes = 10
net = TwoLayerNet(input_size, hidden_size, num_classes)
# Train the network
stats = net.train(
X_train,
y_train,
X_val,
y_val,
num_iters=1000,
batch_size=200,
learning_rate=1e-4,
learning_rate_decay=0.95,
reg=0.5,
verbose=True,
)
hidden_size = [500]
results = {}
number_of_iters = [5000]
learning_rates = [0.5,
0.6] # np.logspace(-10, 10, 8) #-10, -9, -8, -7, -6, -5, -4
regularization_strengths = [3e-3]
batch_sizes = [200]
for hid_size in hidden_size:
hidden_size = hid_size
for reg in regularization_strengths:
for lr in learning_rates:
for num_iter in number_of_iters:
for bs in batch_sizes:
tic = time.time()
net = TwoLayerNet(input_size, hidden_size, num_classes)
print(
"hid_size %d / lr %.2E / reg %.2e / num_iter %d / batch_s %d"
% (hid_size, lr, reg, num_iter, bs))
training_results = net.train(X_train_feats,
y_train,
X_val_feats,
y_val,
num_iters=num_iter,
batch_size=bs,
learning_rate=lr,
learning_rate_decay=0.95,
reg=reg,
verbose=True)
val_acc = (net.predict(X_val_feats) == y_val).mean()
toc = time.time()
# Invoke the above function to get our data.
X_train, y_train, X_val, y_val, X_test, y_test = get_CIFAR10_data()
print('Train data shape: ', X_train.shape)
print('Train labels shape: ', y_train.shape)
print('Validation data shape: ', X_val.shape)
print('Validation labels shape: ', y_val.shape)
print('Test data shape: ', X_test.shape)
print('Test labels shape: ', y_test.shape)
#%% Train a network, we will use SGD, and we will adjust the learning rate with an
# exponential learning rate schedule as optimization proceeds.
input_size = 32 * 32 * 3
hidden_size = 50
num_classes = 10
net = TwoLayerNet(input_size, hidden_size, num_classes)
# Train the network
stats = net.train(X_train,
y_train,
X_val,
y_val,
num_iters=1000,
batch_size=200,
learning_rate=1e-4,
learning_rate_decay=0.95,
reg=0.25,
verbose=True)
# Predict on the validation set
val_acc = (net.predict(X_val) == y_val).mean()
x_test_feats /= std_feat
# Preprocessing: Add a bias dimension
X_train_feats = np.hstack(
[x_train_feats, np.ones((x_train_feats.shape[0], 1))])
X_val_feats = np.hstack([x_val_feats, np.ones((x_val_feats.shape[0], 1))])
X_test_feats = np.hstack([x_test_feats, np.ones((x_test_feats.shape[0], 1))])
print(x_train_feats.shape)
from cs231n.classifiers.neural_net import TwoLayerNet
input_dim = x_train_feats.shape[1]
hidden_dim = 500
num_classes = 10
net = TwoLayerNet(input_dim, hidden_dim, num_classes)
best_net = None
results = {}
best_val = -1
best_net = None
learning_rates = [1e-2, 1e-1, 5e-5, 1, 5]
regularization_strengths = [1e-3, 5e-3, 1e-2, 1e-1, 0.5, 1]
for lr in learning_rates:
for reg in regularization_strengths:
net = TwoLayerNet(input_dim, hidden_dim, num_classes)
#Train the network
stats = net.train(x_train_feats,
y_train,
x_val_feats,
# Ideas: PCA, Dropout, adding features
input_size = 32 * 32 * 3
num_classes = 10
lrates = [0.001]
regs = [0.02]
hidden_sizes = [100]
best_accuracy = 0
for lrate in lrates:
for reg in regs:
for hidden_size in hidden_sizes:
# Train the network with the combination
net = TwoLayerNet(input_size, hidden_size, num_classes)
stats, test_net = net.train(X_train,
y_train,
X_val,
y_val,
num_iters=10000,
batch_size=200,
learning_rate=lrate,
learning_rate_decay=.95,
reg=reg,
verbose=True)
if stats['val_acc_history'][-1] > best_accuracy:
best_net = test_net
best_accuracy = stats['val_acc_history'][-1]
best_loss = np.mean(stats["loss_history"][-10:-1])
def init_toy_model():
np.random.seed(0)
# 以0为种子对网络参数初始化，每次都会得到同样的W和d
return TwoLayerNet(input_size, hidden_size, num_classes, std=1e-1)
input_size = 32 * 32 * 3
hidden_size = 50
num_classes = 10
best_val = -1
best_stats = None
learning_rates = [1e-4,2e-4,5e-4,8e-4]
regularization_strengths = [0.2,0.3,0.4]
results = {}
iters = 2000 #100
for lr in learning_rates:
for rs in regularization_strengths:
net = TwoLayerNet(input_size, hidden_size, num_classes)
# Train the network
stats = net.train(X_train, y_train, X_val, y_val,num_iters=iters, batch_size=200,learning_rate=lr, learning_rate_decay=0.95,reg=rs)
y_train_pred = net.predict(X_train)
acc_train = np.mean(y_train == y_train_pred)
y_val_pred = net.predict(X_val)
acc_val = np.mean(y_val == y_val_pred)
results[(lr, rs)] = (acc_train, acc_val)
if best_val < acc_val:
best_stats = stats
best_val = acc_val
best_net = net
# differences from the ones we saw above for the poorly tuned network. #
# #
# Tweaking hyperparameters by hand can be fun, but you might find it useful to #
# write code to sweep through possible combinations of hyperparameters #
# automatically like we did on the previous exercises. #
#################################################################################
results = {}
best_val = -1
learning_rates = [1e-3]
regularization_strengths = [0.05,0.1,0.5]
hidden_size = [50,100]
for lr in learning_rates:
for reg in regularization_strengths:
for hnum in hidden_size:
net = TwoLayerNet(input_size, hnum, num_classes)
stats = net.train(X_train, y_train, X_val, y_val,
num_iters=1000, batch_size=200,
learning_rate=lr, learning_rate_decay=0.95,
reg=reg, verbose=True)
y_train_pred = net.predict(X_train)
y_val_pred = net.predict(X_val)
tmp_train_accuracy=np.mean(y_train == y_train_pred)
tmp_val_accuracy=np.mean(y_val == y_val_pred)
results[(lr,reg,hnum)]=[tmp_train_accuracy,tmp_val_accuracy]
if tmp_val_accuracy>best_val:
best_val=tmp_val_accuracy
best_net=copy.deepcopy(net)
#################################################################################
# END OF YOUR CODE #
#################################################################################
# cross-validate various parameters as in previous sections. Store your best #
# model in the best_net variable. #
################################################################################
learning_rates = [3e-1, 1]
regularization_strengths = [1e-4, 3e-4, 1e-3, 3e-3]
# learning_rates = np.logspace(-2, 1, 5)
# regularization_strengths = np.logspace(-5, -2, 5)
results = {}
best_val = -1 # The highest validation accuracy that we have seen so far.
for lr in learning_rates:
for reg in regularization_strengths:
net = TwoLayerNet(input_dim, hidden_dim, num_classes)
# Train the network
stats = net.train(X_train_feats, y_train, X_val_feats, y_val,
num_iters=2001, batch_size=200,
learning_rate=lr, learning_rate_decay=0.95,
reg=reg, verbose=False)
y_train_pred = net.predict(X_train_feats)
training_accuracy = np.mean(y_train == y_train_pred)
y_val_pred = net.predict(X_val_feats)
validation_accuracy = np.mean(y_val == y_val_pred)
results[(lr, reg)] = (training_accuracy, validation_accuracy)
if best_val < validation_accuracy:
best_val = validation_accuracy
i * len(classes) + cls + 1)
plt.imshow(X_test[idx].astype('uint8'))
plt.axis('off')
if i == 0:
plt.title(cls_name)
plt.show()
#%%
from cs231n.classifiers.neural_net import TwoLayerNet
input_dim = X_train_feats.shape[1]
hidden_dim = 550
num_classes = 10
net = TwoLayerNet(input_dim, hidden_dim, num_classes)
best_net = None
################################################################################
# Train a two-layer neural network on image features. You may want to #
# cross-validate various parameters as in previous sections. Store your best #
# model in the best_net variable. #
learning_rates = [1, 8e-1, 6e-1]
regularization_strengths = [6e-5, 8e-4, 1e-3]
results = {}
best_val = -1
best_net = None
for lr in learning_rates:
print('Validation labels shape: ', y_val.shape)
print('Test data shape: ', X_test.shape)
print('Test labels shape: ', y_test.shape)
# # Train a network
# To train our network we will use SGD with momentum. In addition,
#we will adjust the learning rate with an exponential learning rate schedule
#as optimization proceeds; after each epoch, we will reduce the learning rate
#by multiplying it by a decay rate.
# In[ ]:
input_size = 32 * 32 * 3
hidden_size = 50
num_classes = 10
net = TwoLayerNet(input_size, hidden_size, num_classes)
# Train the network
stats = net.train(X_train,
y_train,
X_val,
y_val,
num_iters=1000,
batch_size=200,
learning_rate=1e-4,
learning_rate_decay=0.95,
reg=0.25,
verbose=True)
# Predict on the validation set
val_acc = (net.predict(X_val) == y_val).mean()
# Invoke the above function to get our data.
X_train, y_train, X_val, y_val, X_test, y_test = get_CIFAR10_data()
print 'Train data shape: ', X_train.shape
print 'Train labels shape: ', y_train.shape
print 'Validation data shape: ', X_val.shape
print 'Validation labels shape: ', y_val.shape
print 'Test data shape: ', X_test.shape
print 'Test labels shape: ', y_test.shape
#%%
input_size = 32 * 32 * 3
hidden_size = 110
num_classes = 10
net = TwoLayerNet(input_size, hidden_size, num_classes)
# Train the network, experiment with learning rates, regularization
stats = net.train(X_train, y_train, X_val, y_val,
num_iters=2500, batch_size=200,
learning_rate=5e-4, learning_rate_decay=0.95,
reg=1, verbose=True)
# Predict on the validation set
val_acc = np.mean(net.predict(X_val) == y_val)
print 'Validation accuracy: ', val_acc
#%%
# Plot the loss function and train / validation accuracies
plt.subplot(2, 1, 1)
print 'Train labels shape: ', y_train.shape
print 'Validation data shape: ', X_val.shape
print 'Validation labels shape: ', y_val.shape
print 'Test data shape: ', X_test.shape
print 'Test labels shape: ', y_test.shape
# # Train a network
# To train our network we will use SGD with momentum. In addition, we will adjust the learning rate with an exponential learning rate schedule as optimization proceeds; after each epoch, we will reduce the learning rate by multiplying it by a decay rate.
# In[11]:
input_size = 32 * 32 * 3
hidden_size = 50
num_classes = 10
net = TwoLayerNet(input_size, hidden_size, num_classes)
# Train the network
stats = net.train(X_train, y_train, X_val, y_val,
num_iters=1000, batch_size=200,
learning_rate=1e-4, learning_rate_decay=0.95,
reg=0.5, verbose=True)
# Predict on the validation set
val_acc = (net.predict(X_val) == y_val).mean()
print 'Validation accuracy: ', val_acc
# # Debug the training
# With the default parameters we provided above, you should get a validation accuracy of about 0.29 on the validation set. This isn't very good.
# Invoke the above function to get our data.
X_train, y_train, X_val, y_val, X_test, y_test = get_CIFAR10_data()
print('Train data shape: ', X_train.shape)
print('Train labels shape: ', y_train.shape)
print('Validation data shape: ', X_val.shape)
print('Validation labels shape: ', y_val.shape)
print('Test data shape: ', X_test.shape)
print('Test labels shape: ', y_test.shape)
input_size = 32 * 32 * 3
hidden_size = 50
num_classes = 10
net = TwoLayerNet(input_size, hidden_size, num_classes)
# Train the network
stats = net.train(X_train, y_train, X_val, y_val,num_iters=1000, batch_size=200,learning_rate=1e-4, learning_rate_decay=0.95,reg=0.25, verbose=True)
# Predict on the validation set
val_acc = (net.predict(X_val) == y_val).mean()
print('Validation accuracy: ', val_acc)
# Plot the loss function and train / validation accuracies
plt.subplot(2, 1, 1)
plt.plot(stats['loss_history'])
plt.title('Loss history')
plt.xlabel('Iteration')
plt.ylabel('Loss')
plt.subplot(2, 1, 2)
print('Train labels shape: ', y_train.shape)
print('Validation data shape: ', X_val.shape)
print('Validation labels shape: ', y_val.shape)
print('Test data shape: ', X_test.shape)
print('Test labels shape: ', y_test.shape)
# # Train a network
# To train our network we will use SGD with momentum. In addition, we will
# adjust the learning rate with an exponential learning rate schedule as
# optimization proceeds; after each epoch, we will reduce the learning
# rate by multiplying it by a decay rate.
input_size = 32 * 32 * 3
hidden_size = 50
num_classes = 10
net = TwoLayerNet(input_size, hidden_size, num_classes)
# Train the network
stats = net.train(X_train,
y_train,
X_val,
y_val,
num_iters=1000,
batch_size=200,
learning_rate=1e-3,
learning_rate_decay=0.95,
reg=0.25,
verbose=True)
# Predict on the validation set
val_acc = (net.predict(X_val) == y_val).mean()
X_val = X_val.reshape(num_validation, -1)
X_test = X_test.reshape(num_test, -1)
print('Train data shape:', X_train.shape)
print('Train labels shape:', y_train.shape)
print('Validation data shape:', X_val.shape)
print('Validation labels shape:', y_val.shape)
print('Test data shape:', X_test.shape)
print('Test labels shape:', y_test.shape)
#%%
# train a nerwork
input_size = 32*32*3
hidden_size = 81
num_classes = 10
net = TwoLayerNet(input_size, hidden_size, num_classes)
# Train the network
stats = net.train(X_train, y_train, X_val, y_val,
num_iters=1500, batch_size=200,
learning_rate=7.5e-4, learning_rate_decay=0.95,
reg=1.0, verbose=True)
# Predict on the validation set
val_acc = (net.predict(X_val) == y_val).mean()
print('Validation accuracy: ', val_acc)
#%%
# Debug the training
# plot the loss function and train/validation accuracies
# Invoke the above function to get our data.
X_train, y_train, X_val, y_val, X_test, y_test = get_CIFAR10_data()
print('Train data shape: ', X_train.shape)
print('Train labels shape: ', y_train.shape)
print('Validation data shape: ', X_val.shape)
print('Validation labels shape: ', y_val.shape)
print('Test data shape: ', X_test.shape)
print('Test labels shape: ', y_test.shape)
input_size = 32 * 32 * 3
hidden_size = 50
num_classes = 10
net = TwoLayerNet(input_size, hidden_size, num_classes)
# Train the network
stats = net.train(X_train, y_train, X_val, y_val,
num_iters=1000, batch_size=200,
learning_rate=1e-4, learning_rate_decay=0.95,
reg=0.25, verbose=True)
# Predict on the validation set
val_acc = (net.predict(X_val) == y_val).mean()
print('Validation accuracy: ', val_acc)
# Plot the loss function and train / validation accuracies
plt.subplot(2, 1, 1)
#net = TwoLayerNet(input_size, hi_size, num_classes)
#%%
count = 0 # for tracking iterations, visualization, not required for model
#for hi_size in hidden_range:
for lr in learning_range:
for reg in regularization_range:
#n = 0
# lr = learning_range[n]
#reg = regularization_range[n]
count += 1 # track how many times we have calculated the results
print('Calculate %d out of %d' % (count, total_para_sets))
net = TwoLayerNet(input_size, hi_size, num_classes)
# Train the network
stats = net.train(
X_train_feats0,
y_train,
X_val_feats0,
y_val,
num_iters=1000,
batch_size=
400, # increase the number of iteration, and batch_size will help to increase number of epoches
# in the train(), iterations_per_epoch = max(num_train / batch_size, 1), increase batch_size
# will decrease iterations_per_epoch, and increase number of epoches
# iterations // iterations_per_epoch, increase the num_iter, will help increase number of epoches
learning_rate=lr,
learning_rate_decay=0.95,
reg=reg,
def init_toy_model():
np.random.seed(0)
return TwoLayerNet(input_size, hidden_size, num_classes, std=1e-1)
for reg_str in regularization_strengths:
for train_circle in training_epoch:
for lr_rate in learning_rate:
for hid_size in hidden_size:
whole_time -= 1
print "now rest times is:", whole_time
"""
print
print "now rest times is:", whole_time
print "now hidden size is: ", hid_size
print "now learning rate is: ", lr_rate
print "now training epoch is:", train_circle
print "now regualrization streagths is: ", reg_str
print "now batch size is: ", bat_size
"""
net = TwoLayerNet(input_size, hid_size, num_classes)
now_stats = net.train(X_train,
y_train,
X_val,
y_val,
learning_rate=lr_rate,
learning_rate_decay=0.95,
reg=reg_str,
num_iters=train_circle,
batch_size=bat_size,
verbose=False)
y_pred_val = net.predict(X_val)
acc_val = np.mean(y_pred_val == y_val)
print(X_train_feats.shape)
from cs231n.classifiers.neural_net import TwoLayerNet
input_dim = X_train_feats.shape[1]
hidden_dim = 500
num_classes = 10
best_net = None
best_val = -1
learning_rates = [9e-1,7e-1,5e-1,3e-1,1e-1,9e-2,7e-2,5e-2,3e-2]
regularization_strengths = [1e-3,3e-3,5e-3,7e-3,9e-3]
for lr in learning_rates:
for reg in regularization_strengths:
net = TwoLayerNet(input_dim, hidden_dim, num_classes)
net.train(X_train_feats, y_train, X_val_feats, y_val,
learning_rate=lr, learning_rate_decay=0.95,
reg=reg, num_iters=6000, batch_size=200, verbose=False)
train_accuracy = (net.predict(X_train_feats) == y_train).mean()
val_accuracy = (net.predict(X_val_feats) == y_val).mean()
print('lr %e reg %e train accuracy: %f val accuracy: %f' % (lr,reg,train_accuracy,val_accuracy))
if val_accuracy > best_val:
best_val = val_accuracy
best_net = net
print('best validation accuracy achieved during cross-validation: %f' % best_val)
test_accuracy = (best_net.predict(X_test_feats) == y_test).mean()
print(test_accuracy)
def show_net_weights(net):
W1 = net.params['W1']
W1 = W1.reshape(32, 32, 3, -1).transpose(3, 0, 1, 2)
plt.imshow(visualize_grid(W1, padding=3).astype('uint8'))
plt.gca().axis('off')
plt.show()
#show_net_weights(net)
best_net = None
best_loss = float('inf')
rates = [0.1,0.01,0.001]
for rate in rates:
net = TwoLayerNet(input_size, hidden_size, num_classes)
# Train the network
stats = net.train(X_train, y_train, X_val, y_val,
num_iters=1000, batch_size=200,
learning_rate=rate, learning_rate_decay=0.98,
reg=0.3, verbose=False)
loss_avg = np.mean(stats['loss_history'][-1])
print "Rate: ", rate, " loss: ", loss_avg
val_acc = (net.predict(X_val) == y_val).mean()
print 'Validation accuracy: ', val_acc
if val_acc > best_loss:
best_net = net