Fix docstring text, increase default precision for sensitive tests

Author: jmitrevs

test/pytest/test_keras_api.py

@@ -330,7 +330,7 @@ def test_depthwise2d(backend, io_type):
 @pytest.mark.parametrize('io_type', ['io_stream'])
 def test_depthwise1d(backend, io_type):
     '''
-    Test proper handling of QConv2DBatchnorm.
+    Test proper handling of DepthwiseConv1D.
     '''
     X = np.random.rand(10, 32, 3)
     X = np.round(X * 2**10) * 2**-10  # make it an exact ap_fixed<16,6>

test/pytest/test_qkeras.py

@@ -388,7 +388,7 @@ def test_qconv2dbn(randX_100_8_8_1, backend, io_type):
     )
     model.compile()
 
-    config = hls4ml.utils.config_from_keras_model(model, granularity='name')
+    config = hls4ml.utils.config_from_keras_model(model, granularity='name', default_precision='fixed<24,8>')
     output_dir = str(test_root_path / f'hls4mlprj_qkeras_qconv2dbn_{backend}_{io_type}')
     hls_model = hls4ml.converters.convert_from_keras_model(
         model, hls_config=config, output_dir=output_dir, backend=backend, io_type=io_type
@@ -412,7 +412,7 @@ def randX_10_32_32_3():
 @pytest.mark.parametrize('io_type', ['io_stream'])
 def test_qdepthwiseconv2d(randX_10_32_32_3, backend, io_type):
     '''
-    Test proper handling of QConv2DBatchnorm.
+    Test proper handling of QDepthwiseConv2D.
     '''
     X = randX_10_32_32_3
     X = np.round(X * 2**10) * 2**-10  # make it an exact ap_fixed<16,6>
@@ -422,15 +422,14 @@ def test_qdepthwiseconv2d(randX_10_32_32_3, backend, io_type):
             kernel_size=(3, 3),
             input_shape=(32, 32, 3),
             depthwise_quantizer='quantized_bits(6, 0, alpha=1)',
-            depthwise_initializer='ones',
             bias_quantizer='quantized_bits(4, 0, alpha=1)',
-            bias_initializer='zeros',
+            bias_initializer='he_normal',
             activation='quantized_relu(3, 0)',
         )
     )
     model.compile()
 
-    config = hls4ml.utils.config_from_keras_model(model, granularity='name')
+    config = hls4ml.utils.config_from_keras_model(model, granularity='name', default_precision='fixed<24,8>')
     output_dir = str(test_root_path / f'hls4mlprj_qkeras_qdepthwiseconv2d_{backend}_{io_type}')
     hls_model = hls4ml.converters.convert_from_keras_model(
         model, hls_config=config, output_dir=output_dir, backend=backend, io_type=io_type