# Path: code/nn/initialization.py
# USE_XAVIER_INIT = False
# def get_activation_by_name(name):
# def set_default_rng_seed(seed):
# def random_init(size, rng=None, rng_type=None):
# def create_shared(vals, name=None):
# 
# Path: code/nn/initialization.py
# def set_default_rng_seed(seed):
#     global default_rng, default_srng
#     random.seed(seed)
#     default_rng = np.random.RandomState(random.randint(0,9999))
#     default_srng = T.shared_randomstreams.RandomStreams(default_rng.randint(9999))
# 
# def random_init(size, rng=None, rng_type=None):
#     if rng is None: rng = default_rng
#     if rng_type is None:
#         #vals = rng.standard_normal(size)
#         vals = rng.uniform(low=-0.05, high=0.05, size=size)
# 
#     elif rng_type == "normal":
#         vals = rng.standard_normal(size)
# 
#     elif rng_type == "uniform":
#         vals = rng.uniform(low=-3.0**0.5, high=3.0**0.5, size=size)
# 
#     else:
#         raise Exception(
#             "unknown random inittype: {}".format(rng_type)
#           )
# 
#     return vals.astype(theano.config.floatX)
# 
# def create_shared(vals, name=None):
#     return theano.shared(vals, name=name)
# 
# Path: code/nn/initialization.py
# USE_XAVIER_INIT = False
# def get_activation_by_name(name):
# def set_default_rng_seed(seed):
# def random_init(size, rng=None, rng_type=None):
# def create_shared(vals, name=None):


import numpy as np
import theano
import theano.tensor as T
from utils import say
from .initialization import default_srng, default_rng, USE_XAVIER_INIT
from .initialization import set_default_rng_seed, random_init, create_shared
from .initialization import ReLU, sigmoid, tanh, softmax, linear, get_activation_by_name
        self.create_parameters()

        # not implemented yet
        if clip_gradients is True:
            raise Exception("gradient clip not implemented")

    def create_parameters(self):
        n_in, n_out, activation = self.n_in, self.n_out, self.activation
        self.initialize_params(n_in, n_out, activation)

    def initialize_params(self, n_in, n_out, activation):
        if USE_XAVIER_INIT:
            if activation == ReLU:
                scale = np.sqrt(4.0/(n_in+n_out), dtype=theano.config.floatX)
                b_vals = np.ones(n_out, dtype=theano.config.floatX) * 0.01
            elif activation == softmax:
                scale = np.float64(0.001).astype(theano.config.floatX)
                b_vals = np.zeros(n_out, dtype=theano.config.floatX)
            else:
                scale = np.sqrt(2.0/(n_in+n_out), dtype=theano.config.floatX)
                b_vals = np.zeros(n_out, dtype=theano.config.floatX)
            W_vals = random_init((n_in,n_out), rng_type="normal") * scale
        else:
            W_vals = random_init((n_in,n_out))
            if activation == softmax:
                W_vals *= 0.001
            if activation == ReLU:
                b_vals = np.ones(n_out, dtype=theano.config.floatX) * 0.01
            else:
                b_vals = random_init((n_out,))

# Path: code/nn/initialization.py
# USE_XAVIER_INIT = False
# def get_activation_by_name(name):
# def set_default_rng_seed(seed):
# def random_init(size, rng=None, rng_type=None):
# def create_shared(vals, name=None):
# 
# Path: code/nn/initialization.py
# def set_default_rng_seed(seed):
#     global default_rng, default_srng
#     random.seed(seed)
#     default_rng = np.random.RandomState(random.randint(0,9999))
#     default_srng = T.shared_randomstreams.RandomStreams(default_rng.randint(9999))
# 
# def random_init(size, rng=None, rng_type=None):
#     if rng is None: rng = default_rng
#     if rng_type is None:
#         #vals = rng.standard_normal(size)
#         vals = rng.uniform(low=-0.05, high=0.05, size=size)
# 
#     elif rng_type == "normal":
#         vals = rng.standard_normal(size)
# 
#     elif rng_type == "uniform":
#         vals = rng.uniform(low=-3.0**0.5, high=3.0**0.5, size=size)
# 
#     else:
#         raise Exception(
#             "unknown random inittype: {}".format(rng_type)
#           )
# 
#     return vals.astype(theano.config.floatX)
# 
# def create_shared(vals, name=None):
#     return theano.shared(vals, name=name)
# 
# Path: code/nn/initialization.py
# USE_XAVIER_INIT = False
# def get_activation_by_name(name):
# def set_default_rng_seed(seed):
# def random_init(size, rng=None, rng_type=None):
# def create_shared(vals, name=None):


# Path: code/nn/basic.py
import numpy as np
import theano
import theano.tensor as T
from utils import say
from .initialization import default_srng, default_rng, USE_XAVIER_INIT
from .initialization import set_default_rng_seed, random_init, create_shared
from .initialization import ReLU, sigmoid, tanh, softmax, linear, get_activation_by_name

        self.create_parameters()

        # not implemented yet
        if clip_gradients is True:
            raise Exception("gradient clip not implemented")

    def create_parameters(self):
        n_in, n_out, activation = self.n_in, self.n_out, self.activation
        self.initialize_params(n_in, n_out, activation)

    def initialize_params(self, n_in, n_out, activation):
        if USE_XAVIER_INIT:
            if activation == ReLU:
                scale = np.sqrt(4.0/(n_in+n_out), dtype=theano.config.floatX)
                b_vals = np.ones(n_out, dtype=theano.config.floatX) * 0.01
            elif activation == softmax:
                scale = np.float64(0.001).astype(theano.config.floatX)
                b_vals = np.zeros(n_out, dtype=theano.config.floatX)
            else:
                scale = np.sqrt(2.0/(n_in+n_out), dtype=theano.config.floatX)
                b_vals = np.zeros(n_out, dtype=theano.config.floatX)
            W_vals = random_init((n_in,n_out), rng_type="normal") * scale
        else:
            W_vals = random_init((n_in,n_out))
            if activation == softmax:
                W_vals *= 0.001
            if activation == ReLU:
                b_vals = np.ones(n_out, dtype=theano.config.floatX) * 0.01
            else:
                b_vals = random_init((n_out,))

        self.W = create_shared(W_vals, name="W")