Merge 915af7c into b9694eb

.travis.yml

@@ -1,20 +1,16 @@
 dist: trusty
 language: python
 python:
-  - "3.5"
   - "3.6"
 env:
   matrix:
-    - TF_VERSION=1.15.2
-    - TF_VERSION=2.0
+    - TF_VERSION=2.4.0
 # command to install dependencies
 install:
   - pip install --upgrade pip
   - pip install tensorflow==$TF_VERSION
   - pip install coveralls
 script:
-  # Run eager and no-eager tests separately.
-  - nosetests t3f/*_no_eager_test.py --logging-level=WARNING --with-coverage --cover-package=t3f
-  - nosetests -e='*no_eager*' --logging-level=WARNING --with-coverage --cover-package=t3f
+  - nosetests --logging-level=WARNING --with-coverage --cover-package=t3f
 after_success:
   coveralls

docs/installation.rst

@@ -3,7 +3,7 @@
 Installation
 ============
 
-T3f assumes you have Python 3.5 or 3.6 and a working TensorFlow installation (tested versions are from 1.15.2 to 2.0, see here_ for TF installation instructions).
+T3f assumes you have Python 3.6 and a working TensorFlow installation (tested with TF 2.4, see here_ for TF installation instructions).
 
 .. _here: https://www.tensorflow.org/install/
 

docs/requirement.txt

@@ -2,6 +2,6 @@
 # tf is necessary for building the docs with readthedocs.org: they fetch a fresh
 # version of the library on each build and it doesn't import properly without
 # tensorflow being installed.
-tensorflow>=1.15.2,<=2.0
+tensorflow>=2.4,<=2.4
 ipykernel
 nbsphinx

t3f/initializers.py

@@ -84,7 +84,7 @@ def tensor_ones(shape, dtype=tf.float32, name='t3f_tensor_ones'):
   shape = np.array(shape)
   _validate_input_parameters(is_tensor=True, shape=shape)
   num_dims = shape.size
-  tt_rank = np.ones(num_dims + 1)
+  tt_rank = np.ones(num_dims + 1, dtype=np.int)
 
   with tf.name_scope(name):
     tt_cores = num_dims * [None]
@@ -110,7 +110,7 @@ def tensor_zeros(shape, dtype=tf.float32, name='t3f_tensor_zeros'):
   shape = np.array(shape)
   _validate_input_parameters(is_tensor=True, shape=shape)
   num_dims = shape.size
-  tt_rank = np.ones(num_dims + 1)
+  tt_rank = np.ones(num_dims + 1, dtype=np.int)
   tt_cores = num_dims * [None]
   with tf.name_scope(name):
     for i in range(num_dims):
@@ -138,7 +138,7 @@ def eye(shape, dtype=tf.float32, name='t3f_eye'):
   _validate_input_parameters(is_tensor=True, shape=shape)
 
   num_dims = shape.size
-  tt_ranks = np.ones(num_dims + 1)
+  tt_ranks = np.ones(num_dims + 1, dtype=np.int)
 
   with tf.name_scope(name):
     tt_cores = num_dims * [None]
@@ -182,7 +182,7 @@ def matrix_ones(shape, dtype=tf.float32, name='t3f_matrix_ones'):
   _validate_input_parameters(is_tensor=False, shape=shape)
 
   num_dims = shape[0].size
-  tt_rank = np.ones(shape[0].size + 1)
+  tt_rank = np.ones(shape[0].size + 1, dtype=np.int)
 
   with tf.name_scope(name):
     tt_cores = [None] * num_dims
@@ -224,7 +224,7 @@ def matrix_zeros(shape, dtype=tf.float32, name='t3f_matrix_zeros'):
 
   _validate_input_parameters(is_tensor=False, shape=shape)
   num_dims = shape[0].size
-  tt_rank = np.ones(shape[0].size + 1)
+  tt_rank = np.ones(shape[0].size + 1, dtype=np.int)
 
   with tf.name_scope(name):
     tt_cores = [None] * num_dims
@@ -261,7 +261,7 @@ def tensor_with_random_cores(shape, tt_rank=2, mean=0., stddev=1.,
   _validate_input_parameters(is_tensor=True, shape=shape, tt_rank=tt_rank)
   num_dims = shape.size
   if tt_rank.size == 1:
-    tt_rank = tt_rank * np.ones(num_dims - 1)
+    tt_rank = tt_rank * np.ones(num_dims - 1, dtype=np.int)
     tt_rank = np.insert(tt_rank, 0, 1)
     tt_rank = np.append(tt_rank, 1)
 
@@ -304,7 +304,7 @@ def tensor_batch_with_random_cores(shape, tt_rank=2, batch_size=1,
                              batch_size=batch_size)
   num_dims = shape.size
   if tt_rank.size == 1:
-    tt_rank = tt_rank * np.ones(num_dims - 1)
+    tt_rank = tt_rank * np.ones(num_dims - 1, dtype=np.int)
     tt_rank = np.insert(tt_rank, 0, 1)
     tt_rank = np.append(tt_rank, 1)
   tt_rank = tt_rank.astype(int)
@@ -359,7 +359,7 @@ def matrix_with_random_cores(shape, tt_rank=2, mean=0., stddev=1.,
 
   num_dims = shape[0].size
   if tt_rank.size == 1:
-    tt_rank = tt_rank * np.ones(num_dims - 1)
+    tt_rank = tt_rank * np.ones(num_dims - 1, dtype=np.int)
     tt_rank = np.concatenate([[1], tt_rank, [1]])
 
   tt_rank = tt_rank.astype(int)
@@ -417,7 +417,7 @@ def matrix_batch_with_random_cores(shape, tt_rank=2, batch_size=1,
                              batch_size=batch_size)
   num_dims = shape[0].size
   if tt_rank.size == 1:
-    tt_rank = tt_rank * np.ones(num_dims - 1)
+    tt_rank = tt_rank * np.ones(num_dims - 1, dtype=np.int)
     tt_rank = np.concatenate([[1], tt_rank, [1]])
   shape = shape.astype(int)
   tt_rank = tt_rank.astype(int)
@@ -522,7 +522,7 @@ def random_tensor(shape, tt_rank=2, mean=0., stddev=1., dtype=tf.float32,
 
   num_dims = shape.size
   if tt_rank.size == 1:
-    tt_rank = tt_rank * np.ones(num_dims - 1)
+    tt_rank = tt_rank * np.ones(num_dims - 1, dtype=np.int)
     tt_rank = np.insert(tt_rank, 0, 1)
     tt_rank = np.append(tt_rank, 1)
 
@@ -580,7 +580,7 @@ def random_tensor_batch(shape, tt_rank=2, batch_size=1, mean=0., stddev=1.,
                              batch_size=batch_size)
   num_dims = shape.size
   if tt_rank.size == 1:
-    tt_rank = tt_rank * np.ones(num_dims - 1)
+    tt_rank = tt_rank * np.ones(num_dims - 1, dtype=np.int)
     tt_rank = np.insert(tt_rank, 0, 1)
     tt_rank = np.append(tt_rank, 1)
   tt_rank = tt_rank.astype(int)
@@ -647,7 +647,7 @@ def random_matrix(shape, tt_rank=2, mean=0., stddev=1.,
 
   num_dims = shape[0].size
   if tt_rank.size == 1:
-    tt_rank = tt_rank * np.ones(num_dims - 1)
+    tt_rank = tt_rank * np.ones(num_dims - 1, dtype=np.int)
     tt_rank = np.concatenate([[1], tt_rank, [1]])
 
   tt_rank = tt_rank.astype(int)
@@ -718,7 +718,7 @@ def random_matrix_batch(shape, tt_rank=2, batch_size=1, mean=0., stddev=1.,
                              batch_size=batch_size)
   num_dims = shape[0].size
   if tt_rank.size == 1:
-    tt_rank = tt_rank * np.ones(num_dims - 1)
+    tt_rank = tt_rank * np.ones(num_dims - 1, dtype=np.int)
     tt_rank = np.concatenate([[1], tt_rank, [1]])
 
   shape = shape.astype(int)