Searched refs:keras (Results 1 – 11 of 11) sorted by relevance
| /cmsis-nn-latest/Tests/UnitTest/ |
| D | pooling_settings.py | 21 import tf_keras as keras namespace
88 model = keras.models.Sequential()
90 model.add(keras.layers.InputLayer(input_shape=input_shape[1:], batch_size=self.batches))
93 keras.layers.AveragePooling2D(pool_size=(self.filter_y, self.filter_x),
99 keras.layers.MaxPooling2D(pool_size=(self.filter_y, self.filter_x),
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| D | add_mul_settings.py | 21 import tf_keras as keras namespace
94 input1 = keras.layers.Input(shape=input_shape[1:])
95 input2 = keras.layers.Input(shape=input_shape[1:])
97 layer = keras.layers.Add()([input1, input2])
99 layer = keras.layers.Multiply()([input1, input2])
102 out = keras.layers.Lambda(function=lambda x: x)(layer)
103 model = keras.models.Model(inputs=[input1, input2], outputs=out)
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| D | softmax_settings.py | 20 import tf_keras as keras namespace
150 model = keras.models.Sequential()
152 model.add(keras.layers.Softmax(input_shape=input_shape))
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| D | fully_connected_settings.py | 21 import tf_keras as keras namespace
239 model = keras.models.Sequential()
241 keras.layers.InputLayer(input_shape=(self.y_input * self.x_input * self.input_ch, ),
243 …fully_connected_layer = keras.layers.Dense(self.output_ch, activation=None, use_bias=self.generate…
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| D | conv_settings.py | 22 import tf_keras as keras namespace
334 model = keras.models.Sequential()
336 model.add(keras.layers.InputLayer(input_shape=input_shape[1:], batch_size=self.batches))
338 conv_layer = keras.layers.Conv2D(self.output_ch,
352 … depthwise_layer = keras.layers.DepthwiseConv2D(kernel_size=(self.filter_y, self.filter_x),
365 transposed_conv_layer = keras.layers.Conv2DTranspose(self.output_ch,
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| D | lstm_settings.py | 22 import tf_keras as keras namespace
145 input_layer = keras.layers.Input(shape=(self.time_steps, self.number_inputs),
150 lstm_layer = keras.layers.LSTM(units=self.number_units,
154 lstm_layer = keras.layers.LSTM(units=self.number_units,
157 model = keras.Model(input_layer, lstm_layer, name="LSTM")
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| D | test_settings.py | 22 import keras
29 import tf_keras as keras namespace
134 os.path.basename(__file__), tf.__version__, keras.__version__))
456 model.compile(loss=keras.losses.categorical_crossentropy,
457 optimizer=keras.optimizers.Adam(),
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| D | README.md | 36 pip install numpy packaging tensorflow tf-keras~=2.16
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| /cmsis-nn-latest/Tests/UnitTest/RefactoredTestGen/Lib/ |
| D | op_conv.py | 22 import tf_keras as keras namespace
74 model = keras.models.Sequential()
76 … model.add(keras.layers.InputLayer(input_shape=input_shape[1:], batch_size=params["batch_size"]))
78 conv_layer = keras.layers.Conv2D(params["out_ch"],
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| D | op_lstm.py | 24 import tf_keras as keras namespace
48 input_layer = keras.layers.Input(shape=(params["time_steps"], params["input_size"]),
56 lstm_layer = keras.layers.LSTM(units=params["hidden_size"],
61 lstm_layer = keras.layers.LSTM(units=params["hidden_size"],
65 model = keras.Model(input_layer, lstm_layer, name="LSTM")
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| D | test.py | 29 import tf_keras as keras namespace
153 keras_model.compile(loss=keras.losses.categorical_crossentropy,
154 optimizer=keras.optimizers.Adam(),
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