SUPPORT / SAMPLES & SAS NOTES
Problem Note 67830: An error occurs when you train a Convolution Neural Network (CNN) model that contains a one-dimensional transpose convolution layer
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The following error might occur when you train a CNN model (for example, a model that uses a SAS DLPy API fit call):
dlpy_history = dlpy_model.fit(data=train_tbl,
valid_table=test_tbl,
data_specs=data_specs,
optimizer=optimizer)
ERROR: For transpose convolution layer conv2dtranspose_1, the adjusted input size (after paddings) must be larger than or equal to the kernel size.
The issue occurs when you use a kernel height of 1 in the Conv2DTranspose API call to create a transpose convolution layer in one dimension (1D).
For example, the following code uses Conv2DTranspose( . . ., height=1) to perform a 1D up-sampling task:
import dlpy
input_data = dlpy.layers.Input(name="input", n_channels=3, width=80, height=1)
conv_1 = dlpy.layers.Conv1D(16, 8, act='relu')(input_data)
pool_1 = dlpy.layers.Pooling(width=4, height=1)(conv_1)
conv_2 = dlpy.layers.Conv1D(8, 4, act='relu')(pool_1)
pool_2 = dlpy.layers.Pooling(width=2, height=1)(conv_2)
conv_3 = dlpy.layers.Conv1D(8, 2, act='relu')(pool_2)
upsampling_1 = dlpy.layers.Conv2DTranspose(8, width=2, height=1, stride_horizontal=2, stride_vertical=1, act='relu')(conv_3)
upsampling_2 = dlpy.layers.Conv2DTranspose(16, width=4, height=1, stride_horizontal=4, stride_vertical=1, act='relu')(upsampling_1)
adjust_1 = dlpy.layers.Dense(240)(upsampling_2)
adjust_2 = dlpy.layers.Reshape(width=80, height=1, depth=3)(adjust_1)
output_data = dlpy.layers.Segmentation(name="output")(adjust_2)
dlpy_model_name = "conv_ae"
dlpy_model = dlpy.Model(s, inputs=input_data, outputs=output_data)
dlpy_model.compile()
input_data = dlpy.layers.Input(name="input", n_channels=3, width=80, height=1)
conv_1 = dlpy.layers.Conv1D(16, 8, act='relu')(input_data)
pool_1 = dlpy.layers.Pooling(width=4, height=1)(conv_1)
conv_2 = dlpy.layers.Conv1D(8, 4, act='relu')(pool_1)
pool_2 = dlpy.layers.Pooling(width=2, height=1)(conv_2)
conv_3 = dlpy.layers.Conv1D(8, 2, act='relu')(pool_2)
upsampling_1 = dlpy.layers.Conv2DTranspose(8, width=2, height=1, stride_horizontal=2, stride_vertical=1, act='relu')(conv_3)
upsampling_2 = dlpy.layers.Conv2DTranspose(16, width=4, height=1, stride_horizontal=4, stride_vertical=1, act='relu')(upsampling_1)
adjust_1 = dlpy.layers.Dense(240)(upsampling_2)
adjust_2 = dlpy.layers.Reshape(width=80, height=1, depth=3)(adjust_1)
output_data = dlpy.layers.Segmentation(name="output")(adjust_2)
dlpy_model_name = "conv_ae"
dlpy_model = dlpy.Model(s, inputs=input_data, outputs=output_data)
dlpy_model.compile()
Click the Hot Fix tab in this note for a link to instructions about accessing and applying the software update.
Operating System and Release Information
| Product Family | Product | System | Product Release | SAS Release | ||
| Reported | Fixed* | Reported | Fixed* | |||
| SAS System | SAS Visual Data Mining and Machine Learning | Microsoft® Windows® for x64 | 8.5 | 2021.1.1 | Viya | Viya |
| Linux for x64 | 8.5 | 2021.1.1 | Viya | Viya | ||