SYMBOL INDEX (73 symbols across 8 files)

FILE: deepnet/im2col.py
  function get_im2col_indices (line 4) | def get_im2col_indices(x_shape, field_height=3, field_width=3, padding=1...
  function im2col_indices (line 24) | def im2col_indices(x, field_height=3, field_width=3, padding=1, stride=1):
  function col2im_indices (line 39) | def col2im_indices(cols, x_shape, field_height=3, field_width=3, padding=1,

FILE: deepnet/layers.py
  class Conv (line 5) | class Conv():
    method __init__ (line 7) | def __init__(self, X_dim, n_filter, h_filter, w_filter, stride, padding):
    method forward (line 28) | def forward(self, X):
    method backward (line 41) | def backward(self, dout):
  class Maxpool (line 60) | class Maxpool():
    method __init__ (line 62) | def __init__(self, X_dim, size, stride):
    method forward (line 80) | def forward(self, X):
    method backward (line 95) | def backward(self, dout):
  class Flatten (line 111) | class Flatten():
    method __init__ (line 113) | def __init__(self):
    method forward (line 116) | def forward(self, X):
    method backward (line 123) | def backward(self, dout):
  class FullyConnected (line 128) | class FullyConnected():
    method __init__ (line 130) | def __init__(self, in_size, out_size):
    method forward (line 136) | def forward(self, X):
    method backward (line 141) | def backward(self, dout):
  class Batchnorm (line 148) | class Batchnorm():
    method __init__ (line 150) | def __init__(self, X_dim):
    method forward (line 156) | def forward(self, X):
    method backward (line 168) | def backward(self, dout):
  class Dropout (line 189) | class Dropout():
    method __init__ (line 191) | def __init__(self, prob=0.5):
    method forward (line 195) | def forward(self, X):
    method backward (line 200) | def backward(self, dout):
  class ReLU (line 205) | class ReLU():
    method __init__ (line 206) | def __init__(self):
    method forward (line 209) | def forward(self, X):
    method backward (line 213) | def backward(self, dout):
  class sigmoid (line 219) | class sigmoid():
    method __init__ (line 220) | def __init__(self):
    method forward (line 223) | def forward(self, X):
    method backward (line 228) | def backward(self, dout):
  class tanh (line 233) | class tanh():
    method __init__ (line 234) | def __init__(self):
    method forward (line 237) | def forward(self, X):
    method backward (line 242) | def backward(self, dout):

FILE: deepnet/loss.py
  function l2_regularization (line 6) | def l2_regularization(layers, lam=0.001):
  function delta_l2_regularization (line 14) | def delta_l2_regularization(layers, grads, lam=0.001):
  function l1_regularization (line 21) | def l1_regularization(layers, lam=0.001):
  function delta_l1_regularization (line 29) | def delta_l1_regularization(layers, grads, lam=0.001):
  function SoftmaxLoss (line 36) | def SoftmaxLoss(X, y):

FILE: deepnet/nnet.py
  class CNN (line 6) | class CNN:
    method __init__ (line 8) | def __init__(self, layers, loss_func=SoftmaxLoss):
    method forward (line 15) | def forward(self, X):
    method backward (line 20) | def backward(self, dout):
    method train_step (line 27) | def train_step(self, X, y):
    method predict (line 35) | def predict(self, X):
  class RNN (line 40) | class RNN:
    method __init__ (line 42) | def __init__(self, vocab_size, h_size, char_to_idx, idx_to_char):
    method _forward (line 56) | def _forward(self, X, h):
    method _backward (line 72) | def _backward(self, out, y, dh_next, cache):
    method train_step (line 97) | def train_step(self,X_train, y_train, h):
    method predict (line 128) | def predict(self, X):

FILE: deepnet/solver.py
  function get_minibatches (line 8) | def get_minibatches(X, y, minibatch_size,shuffleTag=True):
  function vanilla_update (line 20) | def vanilla_update(params, grads, learning_rate=0.01):
  function momentum_update (line 26) | def momentum_update(velocity, params, grads, learning_rate=0.01, mu=0.9):
  function adagrad_update (line 33) | def adagrad_update(cache, params, grads, learning_rate=0.01):
  function rmsprop_update (line 40) | def rmsprop_update(cache, params, grads, learning_rate=0.01, decay_rate=...
  function sgd (line 47) | def sgd(nnet, X_train, y_train, minibatch_size, epoch, learning_rate, ve...
  function sgd_rnn (line 64) | def sgd_rnn(nnet, X_train, y_train, minibatch_size, epoch, learning_rate...
  function sgd_momentum (line 80) | def sgd_momentum(nnet, X_train, y_train, minibatch_size, epoch, learning...
  function adam (line 127) | def adam(nnet, X_train, y_train, minibatch_size, epoch, learning_rate, v...

FILE: deepnet/utils.py
  function one_hot_encode (line 7) | def one_hot_encode(y, num_class):
  function accuracy (line 16) | def accuracy(y_true, y_pred):
  function softmax (line 20) | def softmax(x):
  function load_mnist (line 25) | def load_mnist(path, num_training=50000, num_test=10000, cnn=True, one_h...
  function load_cifar10 (line 48) | def load_cifar10(path, num_training=1000, num_test=1000):

FILE: run_cnn.py
  function make_mnist_cnn (line 9) | def make_mnist_cnn(X_dim, num_class):
  function make_cifar10_cnn (line 19) | def make_cifar10_cnn(X_dim, num_class):

FILE: run_rnn.py
  function text_to_inputs (line 6) | def text_to_inputs(path):