Searched refs:in_tensor_1 (Results 1 – 7 of 7) sorted by relevance
| /external/tensorflow/tensorflow/lite/python/ |
| D | convert_saved_model_test.py | 53 in_tensor_1 = array_ops.placeholder(
57 out_tensor = in_tensor_1 + in_tensor_2
58 inputs = {"x": in_tensor_1, "y": in_tensor_2}
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| D | lite_test.py | 674 in_tensor_1 = array_ops.placeholder(
678 out_tensor = math_ops.matmul(in_tensor_1, in_tensor_2)
683 [in_tensor_1, in_tensor_2],
817 in_tensor_1 = array_ops.placeholder(
824 out_tensor = math_ops.matmul(in_tensor_1, in_tensor_2, name='output')
828 float_converter = lite.TFLiteConverter.from_session(sess, [in_tensor_1],
835 sess, [in_tensor_1], [out_tensor])
847 in_tensor_1 = array_ops.placeholder(
854 out_tensor = math_ops.matmul(in_tensor_1, in_tensor_2, name='output')
858 sess, [in_tensor_1], [out_tensor])
[all …]
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| D | convert_test.py | 99 in_tensor_1 = array_ops.placeholder(
104 in_tensor_1 + in_tensor_2, min=0., max=1., name="output")
146 in_tensor_1 = array_ops.placeholder(
151 in_tensor_1 + in_tensor_2, min=0., max=1., name="output")
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| D | tflite_convert_test.py | 173 in_tensor_1 = array_ops.placeholder(
178 in_tensor_1 + in_tensor_2, min=0., max=1., name='output',
197 in_tensor_1 = array_ops.placeholder(
202 in_tensor_1 + in_tensor_2, min=0., max=1., name='output')
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| D | metrics_nonportable_test.py | 250 in_tensor_1 = tf.compat.v1.placeholder(
255 out_tensor = in_tensor_1 + in_tensor_2 * variable_node
256 inputs = {'x': in_tensor_1, 'y': in_tensor_2}
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| D | lite_v2_test.py | 1361 in_tensor_1 = tf.compat.v1.placeholder(
1366 out_tensor = in_tensor_1 + in_tensor_2 * variable_node
1367 inputs = {'x': in_tensor_1, 'y': in_tensor_2}
3544 def model(in_tensor_1, in_tensor_2): argument
3545 return tf.matmul(in_tensor_1, in_tensor_2)
3574 def model(in_tensor_1, in_tensor_2): argument
3575 return tf.matmul(in_tensor_1, in_tensor_2, output_type=tf.int32)
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| /external/tensorflow/tensorflow/lite/python/metrics/ |
| D | metrics_nonportable_test.py | 250 in_tensor_1 = tf.compat.v1.placeholder(
255 out_tensor = in_tensor_1 + in_tensor_2 * variable_node
256 inputs = {'x': in_tensor_1, 'y': in_tensor_2}
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