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/cmsis-nn-latest/Tests/UnitTest/RefactoredTestGen/Lib/
Dop_pad.py37 model = keras.models.Sequential()
38 model.add(keras.layers.InputLayer(input_shape=shapes["input_tensor"][1:]))
41 …model.add(keras.layers.ZeroPadding2D(padding=((params["pre_pad_w"], params["post_pad_w"]), (params…
43 …model.add(keras.layers.ZeroPadding2D(padding=((params["pre_pad_h"], params["post_pad_h"]), (params…
47 return model
Dop_pooling.py36 model = keras.models.Sequential()
37 …model.add(keras.layers.InputLayer(input_shape=shapes["input_tensor"][1:], batch_size=shapes["input…
39 model.add(
45 model.add(
54 return model
Dop_transpose.py42 model = keras.Model([input_lhs], [layer])
44 return model
Dop_fully_connected.py206 model = keras.models.Sequential()
207 model.add(
211 model.add(fully_connected_layer)
223 return model
Dop_lstm.py65 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
Dop_maximum_minimum.py52 model = keras.Model([input_1, input_2], [layer])
54 return model
Dop_batch_matmul.py46 model = keras.Model([input_lhs, input_rhs], [layer])
48 return model
Dop_conv.py190 model = keras.models.Sequential()
192 … model.add(keras.layers.InputLayer(input_shape=input_shape[1:], batch_size=params["batch_size"]))
202 model.add(conv_layer)
214 return model
/cmsis-nn-latest/Tests/UnitTest/
Dpooling_settings.py88 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)
Dsoftmax_settings.py150 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))
Dlstm_settings.py157 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)
Dfully_connected_settings.py239 model = keras.models.Sequential()
240 model.add(
244 model.add(fully_connected_layer)
249 self.convert_model(model, inttype)
Dtest_settings.py448 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,
458 n_inputs = len(model.inputs)
465 converter = tf.lite.TFLiteConverter.from_keras_model(model)
486 with open(self.model_path_tflite, "wb") as model:
487 model.write(tflite_model)
Dconv_settings.py334 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)
Dadd_mul_settings.py103 model = keras.models.Model(inputs=[input1, input2], outputs=out)
105 interpreter = self.convert_and_interpret(model, inttype_tf)
DREADME.md193 - `PregeneratedData` - Host local(Not part of GitHub) test data for model creation using Keras in u…
/cmsis-nn-latest/
DREADME.md107 …model. This optimization can be enabled by providing the compiler with a defition of OPTIONAL_REST…