keras_vgg_buddy/models.py
def get_features(self, x, layers):
        '''Evaluate layer outputs for `x`'''
        if not layers:
            return None
        inputs = [self.net.input]
        if self.learning_phase is not None:
            inputs.append(self.learning_phase)
        f = K.function(inputs, [self.get_layer_output(layer_name) for layer_name in layers])
        feature_outputs = f([x])
        features = dict(zip(layers, feature_outputs))
        return features
def get_features(self, x, layers):
        '''Evaluate layer outputs for `x`'''
        if not layers:
            return None
        inputs = [self.net.input]
        if self.learning_phase is not None:
            inputs.append(self.learning_phase)
        f = K.function(inputs, [self.get_layer_output(layer_name) for layer_name in layers])
        feature_outputs = f([x])
        features = dict(zip(layers, feature_outputs))
        return features
def get_features ( self , x , layers ) : if not layers : return None inputs = [ self . net . input ] if self . learning_phase is not None : inputs . append ( self . learning_phase ) f = K . function ( inputs , [ self . get_layer_output ( layer_name ) for layer_name in layers ] ) feature_outputs = f ( [ x ] ) features = dict ( zip ( layers , feature_outputs ) ) return features
Evaluate layer outputs for `x`
Evaluate layer outputs for x