Initial import: grid-bot — grid trading bot for BTC-USDT on Cifra Markets

2026-09-24 13:22:23 +07:00
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+"""
+Comment about tensorflow layers:
+unfortunately instructions on creation of TF layers change constantly,
+and changed way too many times at this point to remember what-compatible-where.
+
+Layers in einops==0.7.0 (and several prior versions)
+ are compatible with TF 2.13
+
+Layers in einops==0.8.0 were re-implemented
+ according to official instructions for TF 2.16
+
+"""
+
+from typing import Dict, Optional, cast
+
+import tensorflow as tf
+from tensorflow.keras.layers import Layer
+
+from . import RearrangeMixin, ReduceMixin
+from ._einmix import _EinmixMixin
+
+__author__ = "Alex Rogozhnikov"
+
+
+class Rearrange(RearrangeMixin, Layer):
+    def build(self, input_shape):
+        pass  # layer does not have any parameters to be initialized
+
+    def call(self, inputs):
+        return self._apply_recipe(inputs)
+
+    def get_config(self):
+        return {"pattern": self.pattern, **self.axes_lengths}
+
+
+class Reduce(ReduceMixin, Layer):
+    def build(self, input_shape):
+        pass  # layer does not have any parameters to be initialized
+
+    def call(self, inputs):
+        return self._apply_recipe(inputs)
+
+    def get_config(self):
+        return {"pattern": self.pattern, "reduction": self.reduction, **self.axes_lengths}
+
+
+class EinMix(_EinmixMixin, Layer):
+    def _create_parameters(self, weight_shape, weight_bound, bias_shape, bias_bound):
+        # this method is called in __init__,
+        #  but we postpone actual creation to build(), as TF instruction suggests
+        self._params = [weight_shape, weight_bound, bias_shape, bias_bound]
+
+    def _create_rearrange_layers(
+        self,
+        pre_reshape_pattern: Optional[str],
+        pre_reshape_lengths: Optional[Dict],
+        post_reshape_pattern: Optional[str],
+        post_reshape_lengths: Optional[Dict],
+    ):
+        self.pre_rearrange = None
+        if pre_reshape_pattern is not None:
+            self.pre_rearrange = Rearrange(pre_reshape_pattern, **cast(dict, pre_reshape_lengths))
+
+        self.post_rearrange = None
+        if post_reshape_pattern is not None:
+            self.post_rearrange = Rearrange(post_reshape_pattern, **cast(dict, post_reshape_lengths))
+
+    def build(self, input_shape):
+        [weight_shape, weight_bound, bias_shape, bias_bound] = self._params
+        self.weight = self.add_weight(
+            shape=weight_shape,
+            initializer=tf.random_uniform_initializer(-weight_bound, weight_bound),
+            trainable=True,
+        )
+
+        if bias_shape is not None:
+            self.bias = self.add_weight(
+                shape=bias_shape,
+                initializer=tf.random_uniform_initializer(-bias_bound, bias_bound),
+                trainable=True,
+            )
+        else:
+            self.bias = None
+
+    def call(self, inputs):
+        if self.pre_rearrange is not None:
+            inputs = self.pre_rearrange(inputs)
+        result = tf.einsum(self.einsum_pattern, inputs, self.weight)
+        if self.bias is not None:
+            result = result + self.bias
+        if self.post_rearrange is not None:
+            result = self.post_rearrange(result)
+        return result
+
+    def get_config(self):
+        return {
+            "pattern": self.pattern,
+            "weight_shape": self.weight_shape,
+            "bias_shape": self.bias_shape,
+            **self.axes_lengths,
+        }