test_conv1d_model.py 文件源码

def setUp(self):
        if (hasattr(keras, '__version__')==False):
            self.keras_version = 0.2 #didn't have the __version__ tag
        else:
            self.keras_version = float(keras.__version__[0:3])
        self.inp = (np.random.randn(10*10*51)
                    .reshape(10,10,51).transpose(0,2,1))
        self.keras_model = keras.models.Sequential()
        conv_layer = keras.layers.convolutional.Convolution1D(
                        nb_filter=2, filter_length=4, subsample_length=2,
                        #re. input_shape=(51,10), that is, putting the channel
                        #axis last; this is actually due to the bug
                        #that seems to date back to v0.2.0...
                        #https://github.com/fchollet/keras/blob/0.2.0/keras/layers/convolutional.py#L88
                        activation="relu", input_shape=(51,10))
        self.keras_model.add(conv_layer)
        self.keras_model.add(keras.layers.convolutional.MaxPooling1D(
                             pool_length=4, stride=2)) 
        if (self.keras_version > 0.2):
            self.keras_model.add(keras.layers.convolutional.AveragePooling1D(
                             pool_length=4, stride=2))
        else:
            pass #there was no average pooling in 0.2.0 it seems
        self.keras_model.add(keras.layers.core.Flatten())
        self.keras_model.add(keras.layers.core.Dense(output_dim=1))
        self.keras_model.add(keras.layers.core.Activation("sigmoid"))
        self.keras_model.compile(loss="mse", optimizer="sgd")

        if (self.keras_version <= 0.3): 
            self.keras_output_fprop_func = compile_func(
                            [self.keras_model.layers[0].input],
                            self.keras_model.layers[-1].get_output(False))
            grad = theano.grad(theano.tensor.sum(
                       self.keras_model.layers[-2].get_output(False)[:,0]),
                       self.keras_model.layers[0].input)
            self.grad_func = theano.function(
                         [self.keras_model.layers[0].input],
                         grad, allow_input_downcast=True)
        else:
            keras_output_fprop_func = compile_func(
                [self.keras_model.layers[0].input,
                 keras.backend.learning_phase()],
                self.keras_model.layers[-1].output)
            self.keras_output_fprop_func =\
                lambda x: keras_output_fprop_func(x,False)
            grad = theano.grad(theano.tensor.sum(
                       self.keras_model.layers[-2].output[:,0]),
                       self.keras_model.layers[0].input)
            grad_func = theano.function(
                         [self.keras_model.layers[0].input,
                          keras.backend.learning_phase()],
                         grad, allow_input_downcast=True,
                         on_unused_input='ignore')
            self.grad_func = lambda x: grad_func(x, False)