diff --git a/core/leras/__init__.py b/core/leras/__init__.py
index 7d9fb2b..ef4efd6 100644
--- a/core/leras/__init__.py
+++ b/core/leras/__init__.py
@@ -1 +1,418 @@
-from .nn import nn
\ No newline at end of file
+import numpy as np
+from core.leras import nn
+tf = nn.tf
+from tensorflow.python.ops import array_ops, random_ops, math_ops, sparse_ops, gradients
+from tensorflow.python.framework import sparse_tensor
+
+def tf_get_value(tensor):
+    return nn.tf_sess.run (tensor)
+nn.tf_get_value = tf_get_value
+
+
+def batch_set_value(tuples):
+    if len(tuples) != 0:
+        with nn.tf.device('/CPU:0'):
+            assign_ops = []
+            feed_dict = {}
+
+            for x, value in tuples:
+                if isinstance(value, nn.tf.Operation) or \
+                    isinstance(value, nn.tf.Variable):
+                    assign_ops.append(value)
+                else:
+                    value = np.asarray(value, dtype=x.dtype.as_numpy_dtype)
+                    assign_placeholder = nn.tf.placeholder( x.dtype.base_dtype, shape=[None]*value.ndim )
+                    assign_op = nn.tf.assign (x, assign_placeholder )
+                    assign_ops.append(assign_op)
+                    feed_dict[assign_placeholder] = value
+
+            nn.tf_sess.run(assign_ops, feed_dict=feed_dict)
+nn.batch_set_value = batch_set_value
+
+def init_weights(weights):
+    ops = []
+
+    ca_tuples_w = []
+    ca_tuples = []
+    for w in weights:
+        initializer = w.initializer
+        for input in initializer.inputs:
+            if "_cai_" in input.name:
+                ca_tuples_w.append (w)
+                ca_tuples.append ( (w.shape.as_list(), w.dtype.as_numpy_dtype) )
+                break
+        else:
+            ops.append (initializer)
+
+    if len(ops) != 0:
+        nn.tf_sess.run (ops)
+
+    if len(ca_tuples) != 0:
+        nn.batch_set_value( [*zip(ca_tuples_w, nn.initializers.ca.generate_batch (ca_tuples))] )
+nn.init_weights = init_weights
+
+def tf_gradients ( loss, vars ):
+    grads = gradients.gradients(loss, vars, colocate_gradients_with_ops=True )
+    gv = [*zip(grads,vars)]
+    for g,v in gv:
+        if g is None:
+            raise Exception(f"Variable {v.name} is declared as trainable, but no tensors flow through it.")
+    return gv
+nn.gradients = tf_gradients
+
+def average_gv_list(grad_var_list, tf_device_string=None):
+    if len(grad_var_list) == 1:
+        return grad_var_list[0]
+
+    e = tf.device(tf_device_string) if tf_device_string is not None else None
+    if e is not None: e.__enter__()
+    result = []
+    for i, (gv) in enumerate(grad_var_list):
+        for j,(g,v) in enumerate(gv):
+            g = tf.expand_dims(g, 0)
+            if i == 0:
+                result += [ [[g], v]  ]
+            else:
+                result[j][0] += [g]
+
+    for i,(gs,v) in enumerate(result):
+        result[i] = ( tf.reduce_mean( tf.concat (gs, 0), 0 ), v )
+    if e is not None: e.__exit__(None,None,None)
+    return result
+nn.average_gv_list = average_gv_list
+
+def average_tensor_list(tensors_list, tf_device_string=None):
+    if len(tensors_list) == 1:
+        return tensors_list[0]
+
+    e = tf.device(tf_device_string) if tf_device_string is not None else None
+    if e is not None: e.__enter__()
+    result = tf.reduce_mean(tf.concat ([tf.expand_dims(t, 0) for t in tensors_list], 0), 0)
+    if e is not None: e.__exit__(None,None,None)
+    return result
+nn.average_tensor_list = average_tensor_list
+
+def concat (tensors_list, axis):
+    """
+    Better version.
+    """
+    if len(tensors_list) == 1:
+        return tensors_list[0]
+    return tf.concat(tensors_list, axis)
+nn.concat = concat
+
+def gelu(x):
+    cdf = 0.5 * (1.0 + tf.nn.tanh((np.sqrt(2 / np.pi) * (x + 0.044715 * tf.pow(x, 3)))))
+    return x * cdf
+nn.gelu = gelu
+
+def upsample2d(x, size=2):
+    if nn.data_format == "NCHW":
+        b,c,h,w = x.shape.as_list()
+        x = tf.reshape (x, (-1,c,h,1,w,1) )
+        x = tf.tile(x, (1,1,1,size,1,size) )
+        x = tf.reshape (x, (-1,c,h*size,w*size) )
+        return x
+    else:
+        return tf.image.resize_nearest_neighbor(x, (x.shape[1]*size, x.shape[2]*size) )
+nn.upsample2d = upsample2d
+
+def resize2d_bilinear(x, size=2):
+    h = x.shape[nn.conv2d_spatial_axes[0]].value
+    w = x.shape[nn.conv2d_spatial_axes[1]].value
+
+    if nn.data_format == "NCHW":
+        x = tf.transpose(x, (0,2,3,1))
+
+    if size > 0:
+        new_size = (h*size,w*size)
+    else:
+        new_size = (h//-size,w//-size)
+
+    x = tf.image.resize(x, new_size, method=tf.image.ResizeMethod.BILINEAR)
+
+    if nn.data_format == "NCHW":
+        x = tf.transpose(x, (0,3,1,2))
+
+    return x
+nn.resize2d_bilinear = resize2d_bilinear
+
+def resize2d_nearest(x, size=2):
+    if size in [-1,0,1]:
+        return x
+
+
+    if size > 0:
+        raise Exception("")
+    else:
+        if nn.data_format == "NCHW":
+            x = x[:,:,::-size,::-size]
+        else:
+            x = x[:,::-size,::-size,:]
+    return x
+
+    h = x.shape[nn.conv2d_spatial_axes[0]].value
+    w = x.shape[nn.conv2d_spatial_axes[1]].value
+
+    if nn.data_format == "NCHW":
+        x = tf.transpose(x, (0,2,3,1))
+
+    if size > 0:
+        new_size = (h*size,w*size)
+    else:
+        new_size = (h//-size,w//-size)
+
+    x = tf.image.resize(x, new_size, method=tf.image.ResizeMethod.NEAREST_NEIGHBOR)
+
+    if nn.data_format == "NCHW":
+        x = tf.transpose(x, (0,3,1,2))
+
+    return x
+nn.resize2d_nearest = resize2d_nearest
+
+def flatten(x):
+    if nn.data_format == "NHWC":
+        # match NCHW version in order to switch data_format without problems
+        x = tf.transpose(x, (0,3,1,2) )
+    return tf.reshape (x, (-1, np.prod(x.shape[1:])) )
+
+nn.flatten = flatten
+
+def max_pool(x, kernel_size=2, strides=2):
+    if nn.data_format == "NHWC":
+        return tf.nn.max_pool(x, [1,kernel_size,kernel_size,1], [1,strides,strides,1], 'SAME', data_format=nn.data_format)
+    else:
+        return tf.nn.max_pool(x, [1,1,kernel_size,kernel_size], [1,1,strides,strides], 'SAME', data_format=nn.data_format)
+
+nn.max_pool = max_pool
+
+def reshape_4D(x, w,h,c):
+    if nn.data_format == "NHWC":
+        # match NCHW version in order to switch data_format without problems
+        x = tf.reshape (x, (-1,c,h,w))
+        x = tf.transpose(x, (0,2,3,1) )
+        return x
+    else:
+        return tf.reshape (x, (-1,c,h,w))
+nn.reshape_4D = reshape_4D
+
+def random_binomial(shape, p=0.0, dtype=None, seed=None):
+    if dtype is None:
+        dtype=tf.float32
+
+    if seed is None:
+        seed = np.random.randint(10e6)
+    return array_ops.where(
+        random_ops.random_uniform(shape, dtype=tf.float16, seed=seed) < p,
+             array_ops.ones(shape, dtype=dtype), array_ops.zeros(shape, dtype=dtype))
+nn.random_binomial = random_binomial
+
+def gaussian_blur(input, radius=2.0):
+    def gaussian(x, mu, sigma):
+        return np.exp(-(float(x) - float(mu)) ** 2 / (2 * sigma ** 2))
+
+    def make_kernel(sigma):
+        kernel_size = max(3, int(2 * 2 * sigma + 1))
+        mean = np.floor(0.5 * kernel_size)
+        kernel_1d = np.array([gaussian(x, mean, sigma) for x in range(kernel_size)])
+        np_kernel = np.outer(kernel_1d, kernel_1d).astype(np.float32)
+        kernel = np_kernel / np.sum(np_kernel)
+        return kernel, kernel_size
+
+    gauss_kernel, kernel_size = make_kernel(radius)
+    padding = kernel_size//2
+    if padding != 0:
+        if nn.data_format == "NHWC":
+            padding = [ [0,0], [padding,padding], [padding,padding], [0,0] ]
+        else:
+            padding = [ [0,0], [0,0], [padding,padding], [padding,padding] ]
+    else:
+        padding = None
+    gauss_kernel = gauss_kernel[:,:,None,None]
+
+    x = input
+    k = tf.tile (gauss_kernel, (1,1,x.shape[nn.conv2d_ch_axis],1) )
+    x = tf.pad(x, padding )
+    x = tf.nn.depthwise_conv2d(x, k, strides=[1,1,1,1], padding='VALID', data_format=nn.data_format)
+    return x
+nn.gaussian_blur = gaussian_blur
+
+def get_gaussian_weights(batch_size, in_ch, resolution, num_scale=5, sigma=(0.5, 1., 2., 4., 8.)):
+    w = np.empty((num_scale, batch_size, in_ch, resolution, resolution))
+    for i in range(num_scale):
+        gaussian = np.exp(-1.*np.arange(-(resolution/2-0.5), resolution/2+0.5)**2/(2*sigma[i]**2))
+        gaussian = np.outer(gaussian, gaussian.reshape((resolution, 1)))  # extend to 2D
+        gaussian = gaussian/np.sum(gaussian)							  # normalization
+        gaussian = np.reshape(gaussian, (1, 1, resolution, resolution)) 	  # reshape to 3D
+        gaussian = np.tile(gaussian, (batch_size, in_ch, 1, 1))
+        w[i, :, :, :, :] = gaussian
+    return w
+
+nn.get_gaussian_weights = get_gaussian_weights
+
+def style_loss(target, style, gaussian_blur_radius=0.0, loss_weight=1.0, step_size=1):
+    def sd(content, style, loss_weight):
+        content_nc = content.shape[ nn.conv2d_ch_axis ]
+        style_nc = style.shape[nn.conv2d_ch_axis]
+        if content_nc != style_nc:
+            raise Exception("style_loss() content_nc != style_nc")
+        c_mean, c_var = tf.nn.moments(content, axes=nn.conv2d_spatial_axes, keep_dims=True)
+        s_mean, s_var = tf.nn.moments(style, axes=nn.conv2d_spatial_axes, keep_dims=True)
+        c_std, s_std = tf.sqrt(c_var + 1e-5), tf.sqrt(s_var + 1e-5)
+        mean_loss = tf.reduce_sum(tf.square(c_mean-s_mean), axis=[1,2,3])
+        std_loss  = tf.reduce_sum(tf.square(c_std-s_std), axis=[1,2,3])
+        return (mean_loss + std_loss) * ( loss_weight / content_nc.value )
+
+    if gaussian_blur_radius > 0.0:
+        target = gaussian_blur(target, gaussian_blur_radius)
+        style = gaussian_blur(style, gaussian_blur_radius)
+
+    return sd( target, style, loss_weight=loss_weight )
+
+nn.style_loss = style_loss
+
+def dssim(img1,img2, max_val, filter_size=11, filter_sigma=1.5, k1=0.01, k2=0.03):
+    if img1.dtype != img2.dtype:
+        raise ValueError("img1.dtype != img2.dtype")
+
+    not_float32 = img1.dtype != tf.float32
+
+    if not_float32:
+        img_dtype = img1.dtype
+        img1 = tf.cast(img1, tf.float32)
+        img2 = tf.cast(img2, tf.float32)
+
+    filter_size = max(1, filter_size)
+
+    kernel = np.arange(0, filter_size, dtype=np.float32)
+    kernel -= (filter_size - 1 ) / 2.0
+    kernel = kernel**2
+    kernel *= ( -0.5 / (filter_sigma**2) )
+    kernel = np.reshape (kernel, (1,-1)) + np.reshape(kernel, (-1,1) )
+    kernel = tf.constant ( np.reshape (kernel, (1,-1)), dtype=tf.float32 )
+    kernel = tf.nn.softmax(kernel)
+    kernel = tf.reshape (kernel, (filter_size, filter_size, 1, 1))
+    kernel = tf.tile (kernel, (1,1, img1.shape[ nn.conv2d_ch_axis ] ,1))
+
+    def reducer(x):
+        return tf.nn.depthwise_conv2d(x, kernel, strides=[1,1,1,1], padding='VALID', data_format=nn.data_format)
+
+    c1 = (k1 * max_val) ** 2
+    c2 = (k2 * max_val) ** 2
+
+    mean0 = reducer(img1)
+    mean1 = reducer(img2)
+    num0 = mean0 * mean1 * 2.0
+    den0 = tf.square(mean0) + tf.square(mean1)
+    luminance = (num0 + c1) / (den0 + c1)
+
+    num1 = reducer(img1 * img2) * 2.0
+    den1 = reducer(tf.square(img1) + tf.square(img2))
+    c2 *= 1.0 #compensation factor
+    cs = (num1 - num0 + c2) / (den1 - den0 + c2)
+
+    ssim_val = tf.reduce_mean(luminance * cs, axis=nn.conv2d_spatial_axes )
+    dssim = (1.0 - ssim_val ) / 2.0
+
+    if not_float32:
+        dssim = tf.cast(dssim, img_dtype)
+    return dssim
+
+nn.dssim = dssim
+
+def space_to_depth(x, size):
+    if nn.data_format == "NHWC":
+        # match NCHW version in order to switch data_format without problems
+        b,h,w,c = x.shape.as_list()
+        oh, ow = h // size, w // size
+        x = tf.reshape(x, (-1, size, oh, size, ow, c))
+        x = tf.transpose(x, (0, 2, 4, 1, 3, 5))
+        x = tf.reshape(x, (-1, oh, ow, size* size* c ))
+        return x
+    else:
+        return tf.space_to_depth(x, size, data_format=nn.data_format)
+nn.space_to_depth = space_to_depth
+
+def depth_to_space(x, size):
+    if nn.data_format == "NHWC":
+        # match NCHW version in order to switch data_format without problems
+
+        b,h,w,c = x.shape.as_list()
+        oh, ow = h * size, w * size
+        oc = c // (size * size)
+
+        x = tf.reshape(x, (-1, h, w, size, size, oc, ) )
+        x = tf.transpose(x, (0, 1, 3, 2, 4, 5))
+        x = tf.reshape(x, (-1, oh, ow, oc, ))
+        return x
+    else:
+        cfg = nn.getCurrentDeviceConfig()
+        if not cfg.cpu_only:
+            return tf.depth_to_space(x, size, data_format=nn.data_format)
+        b,c,h,w = x.shape.as_list()
+        oh, ow = h * size, w * size
+        oc = c // (size * size)
+
+        x = tf.reshape(x, (-1, size, size, oc, h, w, ) )
+        x = tf.transpose(x, (0, 3, 4, 1, 5, 2))
+        x = tf.reshape(x, (-1, oc, oh, ow))
+        return x
+nn.depth_to_space = depth_to_space
+
+def rgb_to_lab(srgb):
+    srgb_pixels = tf.reshape(srgb, [-1, 3])
+    linear_mask = tf.cast(srgb_pixels <= 0.04045, dtype=tf.float32)
+    exponential_mask = tf.cast(srgb_pixels > 0.04045, dtype=tf.float32)
+    rgb_pixels = (srgb_pixels / 12.92 * linear_mask) + (((srgb_pixels + 0.055) / 1.055) ** 2.4) * exponential_mask
+    rgb_to_xyz = tf.constant([
+        #    X        Y          Z
+        [0.412453, 0.212671, 0.019334], # R
+        [0.357580, 0.715160, 0.119193], # G
+        [0.180423, 0.072169, 0.950227], # B
+    ])
+    xyz_pixels = tf.matmul(rgb_pixels, rgb_to_xyz)
+
+    xyz_normalized_pixels = tf.multiply(xyz_pixels, [1/0.950456, 1.0, 1/1.088754])
+
+    epsilon = 6/29
+    linear_mask = tf.cast(xyz_normalized_pixels <= (epsilon**3), dtype=tf.float32)
+    exponential_mask = tf.cast(xyz_normalized_pixels > (epsilon**3), dtype=tf.float32)
+    fxfyfz_pixels = (xyz_normalized_pixels / (3 * epsilon**2) + 4/29) * linear_mask + (xyz_normalized_pixels ** (1/3)) * exponential_mask
+
+    fxfyfz_to_lab = tf.constant([
+        #  l       a       b
+        [  0.0,  500.0,    0.0], # fx
+        [116.0, -500.0,  200.0], # fy
+        [  0.0,    0.0, -200.0], # fz
+    ])
+    lab_pixels = tf.matmul(fxfyfz_pixels, fxfyfz_to_lab) + tf.constant([-16.0, 0.0, 0.0])
+    return tf.reshape(lab_pixels, tf.shape(srgb))
+nn.rgb_to_lab = rgb_to_lab
+
+def total_variation_mse(images):
+    """
+    Same as generic total_variation, but MSE diff instead of MAE
+    """
+    pixel_dif1 = images[:, 1:, :, :] - images[:, :-1, :, :]
+    pixel_dif2 = images[:, :, 1:, :] - images[:, :, :-1, :]
+
+    tot_var = ( tf.reduce_sum(tf.square(pixel_dif1), axis=[1,2,3]) +
+                tf.reduce_sum(tf.square(pixel_dif2), axis=[1,2,3]) )
+    return tot_var
+nn.total_variation_mse = total_variation_mse
+
+
+def pixel_norm(x, axes):
+    return x * tf.rsqrt(tf.reduce_mean(tf.square(x), axis=axes, keepdims=True) + 1e-06)
+nn.pixel_norm = pixel_norm
+        
+"""
+def tf_suppress_lower_mean(t, eps=0.00001):
+    if t.shape.ndims != 1:
+        raise ValueError("tf_suppress_lower_mean: t rank must be 1")
+    t_mean_eps = tf.reduce_mean(t) - eps
+    q = tf.clip_by_value(t, t_mean_eps, tf.reduce_max(t) )
+    q = tf.clip_by_value(q-t_mean_eps, 0, eps)
+    q = q * (t/eps)
+    return q
+"""