Searched refs:input_shape (Results 1 – 12 of 12) sorted by relevance
/cmsis-nn-latest/Tests/UnitTest/RefactoredTestGen/Lib/ |
D | op_transpose.py | 32 input_shape = copy.deepcopy(params["in_dim"]) 33 shapes["input_tensor"] = input_shape 34 shapes["representational_dataset"] = input_shape 39 input_shape = shapes["input_tensor"] 40 input_lhs = keras.layers.Input(batch_input_shape=input_shape) 53 input_shape = params["in_dim"] 57 generated_params["size"] = math.prod(x for x in input_shape) 63 [input_shape[perm[0]], input_shape[perm[1]], 0, 0] 68 [input_shape[perm[0]], input_shape[perm[1]], input_shape[perm[2]], 0] 72 … [input_shape[perm[0]], input_shape[perm[1]], input_shape[perm[2]], input_shape[perm[3]]]
|
D | op_pooling.py | 37 …model.add(keras.layers.InputLayer(input_shape=shapes["input_tensor"][1:], batch_size=shapes["input… 43 input_shape=shapes["input_tensor"][1:])) 49 input_shape=shapes["input_tensor"][1:]))
|
D | op_conv.py | 191 input_shape = (params["batch_size"], params["input_h"], params["input_w"], params["in_ch"]) 192 … model.add(keras.layers.InputLayer(input_shape=input_shape[1:], batch_size=params["batch_size"])) 198 input_shape=input_shape[1:],
|
D | op_pad.py | 38 model.add(keras.layers.InputLayer(input_shape=shapes["input_tensor"][1:]))
|
D | op_fully_connected.py | 208 … keras.layers.InputLayer(input_shape=(params["in_ch"], ), batch_size=params["batch_size"]))
|
/cmsis-nn-latest/Tests/UnitTest/ |
D | pooling_settings.py | 89 input_shape = (self.batches, self.y_input, self.x_input, self.input_ch) 90 model.add(keras.layers.InputLayer(input_shape=input_shape[1:], batch_size=self.batches)) 96 input_shape=input_shape[1:])) 102 input_shape=input_shape[1:]))
|
D | softmax_settings.py | 106 def get_softmax_randomized_input_data(self, input_data, input_shape): argument 109 input_data = tf.reshape(input_data, input_shape) 111 input_data = self.get_randomized_data(input_shape, 151 input_shape = (self.y_input, self.x_input) 152 model.add(keras.layers.Softmax(input_shape=input_shape))
|
D | add_mul_settings.py | 75 input_shape = (1, self.y_input, self.x_input, self.input_ch) 77 input_data1 = self.get_randomized_data(list(input_shape), 81 input_data2 = self.get_randomized_data(list(input_shape), 94 input1 = keras.layers.Input(shape=input_shape[1:]) 95 input2 = keras.layers.Input(shape=input_shape[1:])
|
D | model_extractor.py | 144 def shape_to_config(self, input_shape, filter_shape, output_shape, layer_name): argument 146 [_, self.filter_y, self.filter_x, _] = input_shape 149 [self.batches, self.y_input, self.x_input, self.input_ch] = input_shape 153 [self.batches, self.input_ch] = input_shape 160 [self.y_input, self.x_input] = input_shape 162 if len(input_shape) == 4: 216 input_shape = input_tensor['shape'] 249 self.shape_to_config(input_shape, filter_shape, output_shape, builtin_name) 288 input_shape = input_details[0]['shape'] 289 input_data = self.get_randomized_input_data(input_data, input_shape)
|
D | conv_settings.py | 335 input_shape = (self.batches, self.y_input, self.x_input, self.input_ch) 336 model.add(keras.layers.InputLayer(input_shape=input_shape[1:], batch_size=self.batches)) 342 input_shape=input_shape[1:], 356 input_shape=input_shape[1:], 369 input_shape=input_shape[1:],
|
D | test_settings.py | 272 def get_randomized_input_data(self, input_data, input_shape=None): argument 274 if input_shape is None: 275 input_shape = [self.batches, self.y_input, self.x_input, self.input_ch] 277 input_data = tf.reshape(input_data, input_shape) 279 input_data = self.get_randomized_data(input_shape,
|
D | fully_connected_settings.py | 241 keras.layers.InputLayer(input_shape=(self.y_input * self.x_input * self.input_ch, ),
|