add load/save weights/bias methods
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mlp.py
190
mlp.py
@ -1,5 +1,6 @@
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#!/usr/bin/env python3
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import struct
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import numpy as np
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try:
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import matplotlib.pyplot as mp
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@ -87,6 +88,11 @@ def w_rand_softmax(n, l, factor=0.01):
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class MultiLayerPerceptron(object):
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param_type = {
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"uint32": {"fmt": 'I', "size": 4},
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"float64": {"fmt": 'd', "size": 8},
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}
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functions = {
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"sigmoid": {"function": sigmoid, "derivative": deriv_sigmoid, "w_rand": w_rand_sigmoid, "name": "sigmoid"},
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@ -329,50 +335,13 @@ class MultiLayerPerceptron(object):
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m = 1
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else:
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m = X.shape[1]
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print("lenX,m",len(X),m)
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#print("lenX,m",len(X),m)
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self.prepare(X, m)
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else:
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self.prepare()
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self.propagate()
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return self._A[self._L]
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def get_output(self):
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return self._A[self._L]
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def get_expected_output(self):
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return self._Y
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def get_input(self):
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return self._X
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def get_weights(self):
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return self._W[1:]
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def get_bias(self):
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return self._b[1:]
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def set_flatten_weights(self, W):
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"""Set weights from a flatten list"""
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shapes = [w.shape for w in self._W[1:]]
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sizes = [w.size for w in self._W[1:]]
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flat_size = sum(sizes)
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assert(len(W) == flat_size)
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ini = 0
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for i, (size, shape) in enumerate(zip(sizes, shapes)):
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self._W[1+i] = np.reshape(W[ini:ini+size], shape)
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ini += size
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def set_flatten_bias(self, B):
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"""Set bias from a flatten list"""
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shapes = [b.shape for b in self._b[1:]]
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sizes = [b.size for b in self._b[1:]]
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flat_size = sum(sizes)
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assert(len(B) == flat_size)
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ini = 0
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for i, (size, shape) in enumerate(zip(sizes, shapes)):
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self._b[1+i] = np.reshape(B[ini:ini+size], shape)
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ini += size
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def back_propagation(self, get_cost_function=False):
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"""Back propagation
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@ -495,3 +464,148 @@ class MultiLayerPerceptron(object):
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res = self.minimize_cost(min_cost, max_iter, alpha, plot)
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return res
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def get_output(self):
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return self._A[self._L]
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def get_expected_output(self):
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return self._Y
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def get_input(self):
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return self._X
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def get_weights(self):
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return self._W[1:]
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def get_bias(self):
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return self._b[1:]
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def set_flatten_weights(self, W):
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"""Set weights from a flatten list"""
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shapes = [w.shape for w in self._W[1:]]
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sizes = [w.size for w in self._W[1:]]
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flat_size = sum(sizes)
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assert(len(W) == flat_size)
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ini = 0
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for i, (size, shape) in enumerate(zip(sizes, shapes)):
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self._W[1+i] = np.reshape(W[ini:ini+size], shape)
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ini += size
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def set_flatten_bias(self, B):
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"""Set bias from a flatten list"""
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shapes = [b.shape for b in self._b[1:]]
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sizes = [b.size for b in self._b[1:]]
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flat_size = sum(sizes)
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assert(len(B) == flat_size)
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ini = 0
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for i, (size, shape) in enumerate(zip(sizes, shapes)):
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self._b[1+i] = np.reshape(B[ini:ini+size], shape)
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ini += size
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def load_weights(self, filename):
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""""Load weights from file
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:param filename: the file name
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:return: True if success else False
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"""
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ret = False
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res = MultiLayerPerceptron.load_params(filename)
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if res["result"]:
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ret = True
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self.set_flatten_weights(res["params"])
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return ret
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def save_weights(self, filename):
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"""Save weights to file
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:param filename: the file name
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:return: True if success else False
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"""
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return MultiLayerPerceptron.save_params(self._W[1:], filename)
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def load_bias(self, filename):
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""""Load bias from file
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:param filename: the file name
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:return: True if success else False
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"""
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ret = False
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res = MultiLayerPerceptron.load_params(filename)
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if res["result"]:
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ret = True
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self.set_flatten_bias(res["params"])
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return ret
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def save_bias(self, filename):
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"""Save bias to file
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:param filename: the file name
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:return: True if success else False
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"""
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return MultiLayerPerceptron.save_params(self._b[1:], filename)
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@staticmethod
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def save_params(params, name):
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"""Save features to file
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:param params: the features to save
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:param name: the file name
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:return: True if success else False
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"""
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ret = False
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param_type = MultiLayerPerceptron.param_type
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# nb layers
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nb = len(params)
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# nbs features per layer
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sizes = [p.size for p in params]
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# packing format string
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fmt = "<{fnb}{fsizes}{fparams}".format(
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fnb=param_type["uint32"]["fmt"],
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fsizes=param_type["uint32"]["fmt"]*nb,
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fparams="".join([param_type["float64"]["fmt"]*n for n in sizes])
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)
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# build the list of sizes and all parameters
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data = [nb] + sizes
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for p in params:
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data += p.flatten().tolist()
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print(sizes)
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# packing raw data
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raw = struct.pack(fmt, *data)
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print(len(raw))
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# save
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with open(name, "wb") as f:
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f.write(raw)
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ret = True
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return ret
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@staticmethod
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def load_params(name):
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"""Load features from file.
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A main list contains lists of all parameters for each layer
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:return: dict with results with key: 'result' (True or False), 'params' (if success), 'sizes' (if success)
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"""
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ret = {"result": False}
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param_type = MultiLayerPerceptron.param_type
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with open(name, "rb") as f:
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# get nb layers
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raw = f.read(param_type["uint32"]["size"])
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fmt = "<{fnb}".format(fnb=param_type["uint32"]["fmt"])
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nb = struct.unpack(fmt, raw)[0]
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# get nbs features per layer
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raw = f.read(param_type["uint32"]["size"]*nb)
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fmt = "<{fsizes}".format(fsizes=param_type["uint32"]["fmt"]*nb)
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sizes = struct.unpack(fmt, raw)
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# get params
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total = sum(sizes)
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raw = f.read(param_type["float64"]["size"]*total)
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fmt = "<{fparams}".format(fparams=param_type["float64"]["fmt"]*total)
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params = struct.unpack(fmt, raw)
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# result
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ret["params"] = params
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ret["sizes"] = sizes
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ret["result"] = True
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return ret
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