Apparently fixed ComplexMaxPooling!

cvnn/_version.py

@@ -1 +1 @@
-__version__ = '1.0.1'
+__version__ = '1.0.2'

cvnn/layers.py

@@ -947,9 +947,7 @@ def pool_function(self, inputs, ksize, strides, padding, data_format):
         output, argmax = tf.nn.max_pool_with_argmax(input=abs_in, ksize=ksize, strides=strides,
                                                     padding=padding, data_format=data_format,
                                                     include_batch_in_index=True)
-        if inputs.shape[0] is None:
-            return output
-        shape = output.shape
+        shape = tf.shape(output)
         tf_res = tf.reshape(tf.gather(tf.reshape(inputs, [-1]), argmax), shape)
         # assert np.all(tf_res == output)             # For debugging when the input is real only!
         assert tf_res.dtype == inputs.dtype
@@ -984,7 +982,7 @@ def get_real_equivalent(self):
 __copyright__ = 'Copyright 2020, {project_name}'
 __credits__ = ['{credit_list}']
 __license__ = '{license}'
-__version__ = '1.0.1'
+__version__ = '1.0.2'
 __maintainer__ = 'J. Agustin BARRACHINA'
 __email__ = 'joseagustin.barra@gmail.com; jose-agustin.barrachina@centralesupelec.fr'
 __status__ = '{dev_status}'

docs/index.rst

@@ -3,7 +3,7 @@ Complex-Valued Neural Network (CVNN)
 ====================================
 
 :Author:  J. Agustin Barrachina
-:Version: 1.0.1 of 01/20/2021
+:Version: 1.0.2 of 01/20/2021
 
 
 Content

tests/test_several_datasets.py

@@ -6,6 +6,7 @@
 from cvnn.layers import ComplexDense, ComplexFlatten, ComplexInput
 import cvnn.layers as complex_layers
 from cvnn import layers
+from pdb import set_trace
 from cvnn.montecarlo import run_gaussian_dataset_montecarlo
 
 
@@ -130,17 +131,16 @@ def random_dataset():
                   # run_eagerly=True
                   )
     model.summary()
-
     # Train and evaluate
-    history = model.fit(x_train, y_train, epochs=100, validation_data=(x_test, y_test))
+    history = model.fit(x_train, y_train, epochs=2, validation_data=(x_test, y_test))
     test_loss, test_acc = model.evaluate(x_test, y_test, verbose=2)
 
 
 def test_datasets():
-    cifar10_test()
     random_dataset()
-    # fashion_mnist_example()
-    # mnist_example()
+    cifar10_test()
+    fashion_mnist_example()
+    mnist_example()
     # run_gaussian_dataset_montecarlo(epochs=2, iterations=1)