Merge branch 'master' of github.com:keras-team/keras

Author: fchollet

.kokoro/github/ubuntu/gpu/build.sh

@@ -20,6 +20,8 @@ nvcc --version
 
 cd "src/github/keras"
 pip install -U pip setuptools
+# psutil is used by background log reader
+pip install -U psutil
 
 if [ "$KERAS_BACKEND" == "tensorflow" ]
 then

keras/backend/jax/distribution_lib.py

@@ -200,12 +200,12 @@ def initialize(job_addresses, num_processes, process_id):
                 f"{len(job_addresses)} jobs, but num_processes is "
                 f"{num_processes}"
             )
-        corrdinator_address = job_addresses[0]
+        coordinator_address = job_addresses[0]
     else:
-        corrdinator_address = job_addresses
+        coordinator_address = job_addresses
 
     jax.distributed.initialize(
-        corrdinator_address=corrdinator_address,
+        coordinator_address=coordinator_address,
         num_processes=num_processes,
         process_id=process_id,
     )

keras/backend/jax/distribution_lib_test.py

@@ -50,7 +50,7 @@ def test_device_conversion(self):
     def test_initialize_with_all_job_addresses(self, mock_jax_initialze):
         backend_dlib.initialize("10.0.0.1:1234,10.0.0.2:2345", 2, 0)
         mock_jax_initialze.assert_called_once_with(
-            corrdinator_address="10.0.0.1:1234", num_processes=2, process_id=0
+            coordinator_address="10.0.0.1:1234", num_processes=2, process_id=0
         )
 
     def test_initialize_validate_job_and_process(self):
@@ -63,7 +63,7 @@ def test_initialize_validate_job_and_process(self):
     def test_initialize_with_coordinater_address(self, mock_jax_initialze):
         backend_dlib.initialize("10.0.0.1:1234", 2, 0)
         mock_jax_initialze.assert_called_once_with(
-            corrdinator_address="10.0.0.1:1234", num_processes=2, process_id=0
+            coordinator_address="10.0.0.1:1234", num_processes=2, process_id=0
         )
 
     def test_distribute_tensor(self):

keras/backend/jax/numpy.py

@@ -181,11 +181,13 @@ def zeros(shape, dtype=None):
 
 @sparse.elementwise_unary(linear=False)
 def absolute(x):
+    x = convert_to_tensor(x)
     return jnp.absolute(x)
 
 
 @sparse.elementwise_unary(linear=False)
 def abs(x):
+    x = convert_to_tensor(x)
     return jnp.absolute(x)
 
 
@@ -376,16 +378,19 @@ def concatenate(xs, axis=0):
 
 @sparse.elementwise_unary(linear=True)
 def conjugate(x):
+    x = convert_to_tensor(x)
     return jnp.conjugate(x)
 
 
 @sparse.elementwise_unary(linear=True)
 def conj(x):
+    x = convert_to_tensor(x)
     return jnp.conjugate(x)
 
 
 @sparse.elementwise_unary(linear=True)
 def copy(x):
+    x = convert_to_tensor(x)
     return jnp.copy(x)
 
 
@@ -416,6 +421,8 @@ def count_nonzero(x, axis=None):
 
 
 def cross(x1, x2, axisa=-1, axisb=-1, axisc=-1, axis=None):
+    x1 = convert_to_tensor(x1)
+    x2 = convert_to_tensor(x2)
     return jnp.cross(
         x1,
         x2,
@@ -427,10 +434,12 @@ def cross(x1, x2, axisa=-1, axisb=-1, axisc=-1, axis=None):
 
 
 def cumprod(x, axis=None, dtype=None):
+    x = convert_to_tensor(x)
     return jnp.cumprod(x, axis=axis, dtype=dtype)
 
 
 def cumsum(x, axis=None, dtype=None):
+    x = convert_to_tensor(x)
     return jnp.cumsum(x, axis=axis, dtype=dtype)
 
 
@@ -440,6 +449,7 @@ def diag(x, k=0):
 
 
 def diagonal(x, offset=0, axis1=0, axis2=1):
+    x = convert_to_tensor(x)
     return jnp.diagonal(
         x,
         offset=offset,
@@ -449,6 +459,7 @@ def diagonal(x, offset=0, axis1=0, axis2=1):
 
 
 def diff(a, n=1, axis=-1):
+    a = convert_to_tensor(a)
     return jnp.diff(a, n=n, axis=axis)
 
 
@@ -459,6 +470,8 @@ def digitize(x, bins):
 
 
 def dot(x, y):
+    x = convert_to_tensor(x)
+    y = convert_to_tensor(y)
     return jnp.dot(x, y)
 
 
@@ -483,6 +496,7 @@ def exp(x):
 
 
 def expand_dims(x, axis):
+    x = convert_to_tensor(x)
     if isinstance(x, jax_sparse.BCOO):
         (
             _,
@@ -550,6 +564,7 @@ def identity(n, dtype=None):
 
 @sparse.elementwise_unary(linear=True)
 def imag(x):
+    x = convert_to_tensor(x)
     return jnp.imag(x)
 
 
@@ -561,16 +576,19 @@ def isclose(x1, x2):
 
 @sparse.densifying_unary
 def isfinite(x):
+    x = convert_to_tensor(x)
     return jnp.isfinite(x)
 
 
 @sparse.elementwise_unary(linear=False)
 def isinf(x):
+    x = convert_to_tensor(x)
     return jnp.isinf(x)
 
 
 @sparse.elementwise_unary(linear=False)
 def isnan(x):
+    x = convert_to_tensor(x)
     return jnp.isnan(x)
 
 
@@ -648,6 +666,7 @@ def logical_and(x1, x2):
 
 
 def logical_not(x):
+    x = convert_to_tensor(x)
     return jnp.logical_not(x)
 
 
@@ -698,6 +717,7 @@ def meshgrid(*x, indexing="xy"):
 
 
 def min(x, axis=None, keepdims=False, initial=None):
+    x = convert_to_tensor(x)
     return jnp.min(x, axis=axis, keepdims=keepdims, initial=initial)
 
 
@@ -719,6 +739,7 @@ def moveaxis(x, source, destination):
 
 
 def nan_to_num(x):
+    x = convert_to_tensor(x)
     return jnp.nan_to_num(x)
 
 
@@ -749,6 +770,7 @@ def outer(x1, x2):
 
 
 def pad(x, pad_width, mode="constant", constant_values=None):
+    x = convert_to_tensor(x)
     kwargs = {}
     if constant_values is not None:
         if mode != "constant":
@@ -762,6 +784,7 @@ def pad(x, pad_width, mode="constant", constant_values=None):
 
 
 def prod(x, axis=None, keepdims=False, dtype=None):
+    x = convert_to_tensor(x)
     return jnp.prod(x, axis=axis, keepdims=keepdims, dtype=dtype)
 
 
@@ -781,20 +804,24 @@ def quantile(x, q, axis=None, method="linear", keepdims=False):
 
 
 def ravel(x):
+    x = convert_to_tensor(x)
     return jnp.ravel(x)
 
 
 @sparse.elementwise_unary(linear=True)
 def real(x):
+    x = convert_to_tensor(x)
     return jnp.real(x)
 
 
 @sparse.densifying_unary
 def reciprocal(x):
+    x = convert_to_tensor(x)
     return jnp.reciprocal(x)
 
 
 def repeat(x, repeats, axis=None):
+    x = convert_to_tensor(x)
     return jnp.repeat(x, repeats, axis=axis)
 
 
@@ -818,6 +845,7 @@ def roll(x, shift, axis=None):
 
 @sparse.elementwise_unary(linear=False)
 def sign(x):
+    x = convert_to_tensor(x)
     return jnp.sign(x)
 
 
@@ -848,6 +876,7 @@ def size(x):
 
 
 def sort(x, axis=-1):
+    x = convert_to_tensor(x)
     return jnp.sort(x, axis=axis)
 
 
@@ -867,6 +896,7 @@ def std(x, axis=None, keepdims=False):
 
 
 def swapaxes(x, axis1, axis2):
+    x = convert_to_tensor(x)
     return jnp.swapaxes(x, axis1=axis1, axis2=axis2)
 
 
@@ -910,6 +940,7 @@ def tensordot(x1, x2, axes=2):
 
 @sparse.elementwise_unary(linear=False)
 def round(x, decimals=0):
+    x = convert_to_tensor(x)
     return jnp.round(x, decimals=decimals)
 
 
@@ -931,14 +962,18 @@ def tri(N, M=None, k=0, dtype=None):
 
 
 def tril(x, k=0):
+    x = convert_to_tensor(x)
     return jnp.tril(x, k=k)
 
 
 def triu(x, k=0):
+    x = convert_to_tensor(x)
     return jnp.triu(x, k=k)
 
 
 def vdot(x1, x2):
+    x1 = convert_to_tensor(x1)
+    x2 = convert_to_tensor(x2)
     return jnp.vdot(x1, x2)
 
 
@@ -975,11 +1010,13 @@ def power(x1, x2):
 
 @sparse.elementwise_unary(linear=True)
 def negative(x):
+    x = convert_to_tensor(x)
     return jnp.negative(x)
 
 
 @sparse.elementwise_unary(linear=False)
 def square(x):
+    x = convert_to_tensor(x)
     return jnp.square(x)
 
 

keras/backend/numpy/linalg.py

@@ -3,7 +3,7 @@
 
 from keras.backend import standardize_dtype
 from keras.backend.common import dtypes
-from keras.backend.torch.core import convert_to_tensor
+from keras.backend.numpy.core import convert_to_tensor
 
 
 def cholesky(a):

keras/layers/convolutional/base_conv.py

@@ -104,7 +104,7 @@ def __init__(
         )
         self.rank = rank
         self.filters = filters
-        self.groups = groups or 1
+        self.groups = groups
         self.kernel_size = standardize_tuple(kernel_size, rank, "kernel_size")
         self.strides = standardize_tuple(strides, rank, "strides")
         self.dilation_rate = standardize_tuple(
@@ -129,6 +129,12 @@ def __init__(
                 f"positive value. Received filters={self.filters}."
             )
 
+        if self.groups <= 0:
+            raise ValueError(
+                "The number of groups must be a positive integer. "
+                f"Received: groups={self.groups}."
+            )
+
         if self.filters is not None and self.filters % self.groups != 0:
             raise ValueError(
                 "The number of filters must be evenly divisible by the "

keras/layers/convolutional/base_depthwise_conv.py

@@ -132,7 +132,7 @@ def __init__(
         if self.depth_multiplier is not None and self.depth_multiplier <= 0:
             raise ValueError(
                 "Invalid value for argument `depth_multiplier`. Expected a "
-                "strictly  positive value. Received "
+                "strictly positive value. Received "
                 f"depth_multiplier={self.depth_multiplier}."
             )
 

keras/layers/convolutional/base_separable_conv.py

@@ -135,7 +135,7 @@ def __init__(
         if self.depth_multiplier is not None and self.depth_multiplier <= 0:
             raise ValueError(
                 "Invalid value for argument `depth_multiplier`. Expected a "
-                "strictly  positive value. Received "
+                "strictly positive value. Received "
                 f"depth_multiplier={self.depth_multiplier}."
             )