TypeError: unsupported operand type(s) for *: 'int' and 'NoneType' > 35 return umr_prod(a, axis, dtype, out, keepdims)ģ7 def _any(a, axis=None, dtype=None, out=None, keepdims=False): usr/local/lib/python2.7/site-packages/numpy/core/_methods.pyc in _prod(a, axis, dtype, out, keepdims)ģ4 def _prod(a, axis=None, dtype=None, out=None, keepdims=False): usr/local/lib/python2.7/site-packages/numpy/core/fromnumeric.pyc in prod(a, axis, dtype, out, keepdims)Ģ480 return _methods._prod(a, axis=axis, dtype=dtype,Ģ482 return prod(axis=axis, dtype=dtype, out=out) usr/local/lib/python2.7/site-packages/Keras-0.3.1-py2.7.egg/keras/backend/tensorflow_backend.pyc in batch_flatten(x) > 173 previous_output = _output(train=train)ġ74 if hasattr(self, 'layer_cache') and self.cache_enabled:ġ75 previous_layer_id = '%s_%s' % (id(self.previous), train) usr/local/lib/python2.7/site-packages/Keras-0.3.1-py2.7.egg/keras/layers/core.pyc in get_input(self, train)ġ71 if previous_layer_id in self.layer_cache:ġ72 return self.layer_cache usr/local/lib/python2.7/site-packages/Keras-0.3.1-py2.7.egg/keras/layers/core.pyc in get_output(self, train)ĩ63 output = self.activation(K.dot(X, self.W) + self.b) usr/local/lib/python2.7/site-packages/Keras-0.3.1-py2.7.egg/keras/layers/containers.pyc in get_output(self, train) > 435 self.y_train = self.get_output(train=True)Ĥ36 self.y_test = self.get_output(train=False) usr/local/lib/python2.7/site-packages/Keras-0.3.1-py2.7.egg/keras/models.pyc in compile(self, optimizer, loss, class_mode)Ĥ33 self.X_test = self.get_input(train=False) > 12 pile(optimizer=sgd, loss='categorical_crossentropy') TypeError Traceback (most recent call last)ġ1 sgd = SGD(lr=0.1, decay=1e-6, momentum=0.9, nesterov=True) compile( optimizer = sgd, loss = 'categorical_crossentropy') Sgd = SGD( lr = 0.1, decay = 1e-6, momentum = 0.9, nesterov = True) add( Dense( 1000, activation = 'softmax')) add( Convolution2D( 512, 3, 3, activation = 'relu')) add( Convolution2D( 256, 3, 3, activation = 'relu')) add( Convolution2D( 128, 3, 3, activation = 'relu')) add( Convolution2D( 64, 3, 3, activation = 'relu')) convolutional import Convolution2D, MaxPooling2D, ZeroPadding2D core import Flatten, Dense, Dropoutįrom keras.
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