Searched refs:model (Results 1 – 10 of 10) sorted by relevance
| /cmsis-nn-latest/Tests/UnitTest/ |
| D | pooling_settings.py | 88 model = keras.models.Sequential()
90 model.add(keras.layers.InputLayer(input_shape=input_shape[1:], batch_size=self.batches))
92 model.add(
98 model.add(
106 interpreter = self.convert_and_interpret(model, inttype, input_data)
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| D | softmax_settings.py | 150 model = keras.models.Sequential()
152 model.add(keras.layers.Softmax(input_shape=input_shape))
154 … interpreter = self.convert_and_interpret(model, inttype, tf.expand_dims(input_data, axis=0))
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| D | lstm_settings.py | 157 model = keras.Model(input_layer, lstm_layer, name="LSTM")
167 print("Updating weights", model.layers[1 + time_major_offset].weights[0].name)
168 model.layers[1 + time_major_offset].weights[0].assign(weights)
169 print("Updating hidden weights", model.layers[1 + time_major_offset].weights[1].name)
170 model.layers[1 + time_major_offset].weights[1].assign(hidden_weights)
171 print("Updating bias", model.layers[1 + time_major_offset].weights[2].name)
172 model.layers[1 + time_major_offset].weights[2].assign(biases)
174 interpreter = self.convert_and_interpret(model, tf.int8, input_data, dataset_shape=shape)
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| D | fully_connected_settings.py | 239 model = keras.models.Sequential()
240 model.add(
244 model.add(fully_connected_layer)
249 self.convert_model(model, inttype)
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| D | test_settings.py | 448 def convert_and_interpret(self, model, inttype, input_data=None, dataset_shape=None): argument
452 self.convert_model(model, inttype, dataset_shape)
455 def convert_model(self, model, inttype, dataset_shape=None, int16x8_int32bias=False): argument
456 model.compile(loss=keras.losses.categorical_crossentropy,
459 n_inputs = len(model.inputs)
466 converter = tf.lite.TFLiteConverter.from_keras_model(model)
485 with open(self.model_path_tflite, "wb") as model:
486 model.write(tflite_model)
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| D | conv_settings.py | 334 model = keras.models.Sequential()
336 model.add(keras.layers.InputLayer(input_shape=input_shape[1:], batch_size=self.batches))
346 model.add(conv_layer)
359 model.add(depthwise_layer)
373 model.add(transposed_conv_layer)
389 self.convert_model(model, inttype, int16x8_int32bias=self.int16xint8_int32)
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| D | add_mul_settings.py | 103 model = keras.models.Model(inputs=[input1, input2], outputs=out)
105 interpreter = self.convert_and_interpret(model, inttype_tf)
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| D | README.md | 40 …it tests use a Keras generated model for reference. The SVDF unit test use a json template as inpu…
178 - `PregeneratedData` - Host local(Not part of GitHub) test data for model creation using Keras in u…
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| /cmsis-nn-latest/Tests/UnitTest/RefactoredTestGen/Lib/ |
| D | op_conv.py | 74 model = keras.models.Sequential()
76 … model.add(keras.layers.InputLayer(input_shape=input_shape[1:], batch_size=params["batch_size"]))
86 model.add(conv_layer)
98 return model
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| D | op_lstm.py | 65 model = keras.Model(input_layer, lstm_layer, name="LSTM")
68 model.layers[1 + time_major_offset].weights[0].assign(input_weights)
71 model.layers[1 + time_major_offset].weights[1].assign(hidden_weights)
74 model.layers[1 + time_major_offset].weights[2].assign(biases)
76 return model
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