test_conv2d_model_tensorflow_ordering.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__[0:2])
        if (self.keras_version <= 0.2): 
            pass
        else:
            self.inp = (np.random.randn(10*10*51*51)
                        .reshape(10,10,51,51).transpose(0,2,3,1))
            self.keras_model = keras.models.Sequential()
            conv_layer = keras.layers.convolutional.Convolution2D(
                            nb_filter=2, nb_row=4, nb_col=4, subsample=(2,2),
                            activation="relu", input_shape=(51,51,10),
                            dim_ordering='tf')
            self.keras_model.add(conv_layer)
            self.keras_model.add(keras.layers.convolutional.MaxPooling2D(
                                 pool_size=(4,4), strides=(2,2),
                                 dim_ordering='tf')) 
            self.keras_model.add(keras.layers.convolutional.AveragePooling2D(
                                 pool_size=(4,4), strides=(2,2),
                                 dim_ordering='tf')) 
            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,
                             on_unused_input='ignore')
            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)