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Searched refs:input_shape (Results 1 – 12 of 12) sorted by relevance

/cmsis-nn-latest/Tests/UnitTest/RefactoredTestGen/Lib/
Dop_transpose.py32 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]]]
Dop_pooling.py37 …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:]))
Dop_conv.py191 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:],
Dop_pad.py38 model.add(keras.layers.InputLayer(input_shape=shapes["input_tensor"][1:]))
Dop_fully_connected.py208 … keras.layers.InputLayer(input_shape=(params["in_ch"], ), batch_size=params["batch_size"]))
/cmsis-nn-latest/Tests/UnitTest/
Dpooling_settings.py89 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:]))
Dsoftmax_settings.py106 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))
Dadd_mul_settings.py75 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:])
Dmodel_extractor.py144 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)
Dconv_settings.py335 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:],
Dtest_settings.py272 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,
Dfully_connected_settings.py241 keras.layers.InputLayer(input_shape=(self.y_input * self.x_input * self.input_ch, ),