diff --git a/core/leras/layers/SeparableConv2D.py b/core/leras/layers/SeparableConv2D.py
new file mode 100644
index 0000000..3899c2e
--- /dev/null
+++ b/core/leras/layers/SeparableConv2D.py
@@ -0,0 +1,103 @@
+import numpy as np
+from core.leras import nn
+tf = nn.tf
+
+class SeparableConv2D(nn.LayerBase):
+    """
+    default kernel_initializer - CA
+    use_wscale  bool enables equalized learning rate, if kernel_initializer is None, it will be forced to random_normal
+    """
+    def __init__(self, in_ch, out_ch, kernel_size, depth_multiplier=1, strides=1, padding='SAME', dilations=1, use_bias=True, kernel_initializer=None, bias_initializer=None, trainable=True, dtype=None, **kwargs ):
+        if not isinstance(strides, int):
+            raise ValueError ("strides must be an int type")
+        if not isinstance(dilations, int):
+            raise ValueError ("dilations must be an int type")
+        kernel_size = int(kernel_size)
+
+        if dtype is None:
+            dtype = nn.floatx
+
+        if isinstance(padding, str):
+            if padding == "SAME":
+                padding = ( (kernel_size - 1) * dilations + 1 ) // 2
+            elif padding == "VALID":
+                padding = 0
+            else:
+                raise ValueError ("Wrong padding type. Should be VALID SAME or INT or 4x INTs")
+
+        if isinstance(padding, int):
+            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
+
+        if nn.data_format == "NHWC":
+            strides = [1,strides,strides,1]
+        else:
+            strides = [1,1,strides,strides]
+
+        if nn.data_format == "NHWC":
+            dilations = [dilations,dilations]
+        else:
+            dilations = [dilations,dilations]
+
+        self.in_ch = in_ch
+        self.out_ch = out_ch
+        self.kernel_size = kernel_size
+        self.depth_multiplier = depth_multiplier
+        self.strides = strides
+        self.padding = padding
+        self.dilations = dilations
+        self.use_bias = use_bias
+        self.kernel_initializer = kernel_initializer
+        self.bias_initializer = bias_initializer
+        self.trainable = trainable
+        self.dtype = dtype
+        super().__init__(**kwargs)
+
+    def build_weights(self):
+        kernel_initializer = self.kernel_initializer
+
+        if kernel_initializer is None:
+            kernel_initializer = nn.initializers.ca()
+
+        self.depthwise_kernel = tf.get_variable("depthwise_kernel", (self.kernel_size,self.kernel_size,self.in_ch,self.depth_multiplier), dtype=self.dtype, initializer=kernel_initializer, trainable=self.trainable )
+        self.pointwise_kernel = tf.get_variable("pointwise_kernel", (1,1,self.depth_multiplier*self.in_ch,self.out_ch), dtype=self.dtype, initializer=kernel_initializer, trainable=self.trainable )
+
+        if self.use_bias:
+            bias_initializer = self.bias_initializer
+            if bias_initializer is None:
+                bias_initializer = tf.initializers.zeros(dtype=self.dtype)
+
+            self.bias = tf.get_variable("bias", (self.out_ch,), dtype=self.dtype, initializer=bias_initializer, trainable=self.trainable )
+
+    def get_weights(self):
+        weights = [self.depthwise_kernel, self.pointwise_kernel]
+        if self.use_bias:
+            weights += [self.bias]
+        return weights
+
+    def forward(self, x):
+        depthwise_kernel = self.depthwise_kernel
+        pointwise_kernel = self.pointwise_kernel
+
+        if self.padding is not None:
+            x = tf.pad (x, self.padding, mode='CONSTANT')
+
+        x = tf.nn.separable_conv2d(x, depthwise_kernel, pointwise_kernel, self.strides, 'VALID', self.dilations, data_format=nn.data_format)
+        if self.use_bias:
+            if nn.data_format == "NHWC":
+                bias = tf.reshape (self.bias, (1,1,1,self.out_ch) )
+            else:
+                bias = tf.reshape (self.bias, (1,self.out_ch,1,1) )
+            x = tf.add(x, bias)
+        return x
+
+    def __str__(self):
+        r = f"{self.__class__.__name__} : in_ch:{self.in_ch} out_ch:{self.out_ch} "
+
+        return r
+nn.SeparableConv2D = SeparableConv2D
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