dadel/MultiLayerPerceptron
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update prepare and compute output for using given inputs

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dadel 2018-01-19 22:50:52 +01:00
parent a3011adf31
commit d0fc2bd3e9
1 changed files with 41 additions and 15 deletions
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mlp.py
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@ -229,7 +229,7 @@ class MultiLayerPerceptron(object):
assert(len(X) == m) assert(len(X) == m)
self._X = np.matrix(X).T self._X = np.matrix(X).T
else: else:
print(X.shape, self._n[0], m) #print(X.shape, self._n[0], m)
assert(X.shape == (self._n[0], m)) assert(X.shape == (self._n[0], m))
self._X = X self._X = X
self._m = m self._m = m
@ -268,14 +268,37 @@ class MultiLayerPerceptron(object):
self.set_all_input_examples(X, m) self.set_all_input_examples(X, m)
self.set_all_expected_output_examples(Y, m) self.set_all_expected_output_examples(Y, m)
def prepare(self): def prepare(self, X=None, m=None, force=False):
"""Prepare network for propagation""" """Prepare network for propagation"""
if self._prepared == False: if X is not None:
force = True
#print("sforce,prep =", force, self._prepared)
if force or self._prepared == False:
if not force:
self._prepared = True self._prepared = True
if X is None:
assert(self._X is not None) assert(self._X is not None)
X = self._X
if m is None:
m = self._m
if m == 0:
if type(X) is list:
m = 1
else:
m = X.shape[1]
else:
if m is None:
if type(X) is list:
m = 1
else:
m = X.shape[1]
if type(X) is list:
X = np.array(X).reshape(len(X), 1)
if m is None:
assert(self._m > 0) assert(self._m > 0)
m = self._m m = self._m
self._A = [self._X] #print("m prep =", m)
self._A = [X]
self._A += [np.empty((self._n[l+1], m)) for l in range(self._L)] self._A += [np.empty((self._n[l+1], m)) for l in range(self._L)]
self._Z = [None] + [np.empty((self._n[l+1], m)) for l in range(self._L)] self._Z = [None] + [np.empty((self._n[l+1], m)) for l in range(self._L)]
@ -291,7 +314,7 @@ class MultiLayerPerceptron(object):
self._A[l] = self._g[l]["function"](self._Z[l]) self._A[l] = self._g[l]["function"](self._Z[l])
return self._A[self._L] return self._A[self._L]
def compute_outputs(self, X=None): def compute_outputs(self, X=None, m=None):
"""Compute outputs with forward propagation. """Compute outputs with forward propagation.
Note: if no input provided, then the input should have been set using Note: if no input provided, then the input should have been set using
either `set_all_input_examples()` or `set_all_training_examples()`. either `set_all_input_examples()` or `set_all_training_examples()`.
@ -301,11 +324,14 @@ class MultiLayerPerceptron(object):
""" """
if X is not None: if X is not None:
if m is None:
if type(X) is list: if type(X) is list:
m = len(X) m = 1
else: else:
m = X.shape[1] m = X.shape[1]
self.set_all_input_examples(X, m) print("lenX,m",len(X),m)
self.prepare(X, m)
else:
self.prepare() self.prepare()
self.propagate() self.propagate()
return self._A[self._L] return self._A[self._L]