Part 1/3 of the feature sync to the Keras 2.2.4 API.
PiperOrigin-RevId: 216211279
This commit is contained in:
Francois Chollet
TensorFlower Gardener
parent
153decedef
commit
8ef3e7c8c0
tensorflow
python/keras
activations.pyactivations_test.pybackend.pybackend_test.pycallbacks.py
engine
layers
testing_utils.pyutils
tools/api/golden
v1
tensorflow.keras.activations.pbtxttensorflow.keras.backend.pbtxttensorflow.keras.layers.-average-pooling1-d.pbtxttensorflow.keras.layers.-avg-pool1-d.pbtxttensorflow.keras.layers.-conv2-d-transpose.pbtxttensorflow.keras.layers.-conv3-d-transpose.pbtxttensorflow.keras.layers.-convolution2-d-transpose.pbtxttensorflow.keras.layers.-convolution3-d-transpose.pbtxttensorflow.keras.layers.-global-average-pooling1-d.pbtxttensorflow.keras.layers.-global-avg-pool1-d.pbtxttensorflow.keras.layers.-global-max-pool1-d.pbtxttensorflow.keras.layers.-global-max-pooling1-d.pbtxttensorflow.keras.layers.-max-pool1-d.pbtxttensorflow.keras.layers.-max-pooling1-d.pbtxttensorflow.keras.utils.pbtxt
v2
tensorflow.keras.activations.pbtxttensorflow.keras.backend.pbtxttensorflow.keras.layers.-average-pooling1-d.pbtxttensorflow.keras.layers.-avg-pool1-d.pbtxttensorflow.keras.layers.-conv2-d-transpose.pbtxttensorflow.keras.layers.-conv3-d-transpose.pbtxttensorflow.keras.layers.-convolution2-d-transpose.pbtxttensorflow.keras.layers.-convolution3-d-transpose.pbtxttensorflow.keras.layers.-global-average-pooling1-d.pbtxttensorflow.keras.layers.-global-avg-pool1-d.pbtxttensorflow.keras.layers.-global-max-pool1-d.pbtxttensorflow.keras.layers.-global-max-pooling1-d.pbtxttensorflow.keras.layers.-max-pool1-d.pbtxttensorflow.keras.layers.-max-pooling1-d.pbtxttensorflow.keras.utils.pbtxt
@ -160,6 +160,11 @@ def sigmoid(x):
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return nn.sigmoid(x)
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@tf_export('keras.activations.exponential')
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def exponential(x):
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return math_ops.exp(x)
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@tf_export('keras.activations.hard_sigmoid')
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def hard_sigmoid(x):
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"""Hard sigmoid activation function.
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@ -169,6 +169,16 @@ class KerasActivationsTest(test.TestCase):
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expected = np.tanh(test_values)
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self.assertAllClose(result, expected, rtol=1e-05)
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def test_exponential(self):
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with self.cached_session():
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test_values = np.random.random((2, 5))
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x = keras.backend.placeholder(ndim=2)
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exp = keras.activations.exponential(x)
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f = keras.backend.function([x], [exp])
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result = f([test_values])[0]
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expected = np.exp(test_values)
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self.assertAllClose(result, expected, rtol=1e-05)
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def test_linear(self):
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x = np.random.random((10, 5))
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self.assertAllClose(x, keras.activations.linear(x))
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@ -2223,7 +2223,7 @@ def normalize_batch_in_training(x, gamma, beta, reduction_axes, epsilon=1e-3):
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@tf_export('keras.backend.batch_normalization')
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def batch_normalization(x, mean, var, beta, gamma, epsilon=1e-3):
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def batch_normalization(x, mean, var, beta, gamma, axis=-1, epsilon=1e-3):
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"""Applies batch normalization on x given mean, var, beta and gamma.
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I.e. returns:
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@ -2235,11 +2235,49 @@ def batch_normalization(x, mean, var, beta, gamma, epsilon=1e-3):
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var: Variance of batch.
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beta: Tensor with which to center the input.
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gamma: Tensor by which to scale the input.
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axis: Integer, the axis that should be normalized.
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(typically the features axis).
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epsilon: Fuzz factor.
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Returns:
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A tensor.
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"""
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if ndim(x) == 4:
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# The CPU implementation of `fused_batch_norm` only supports NHWC
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if axis == 1 or axis == -3:
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tf_data_format = 'NCHW'
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elif axis == 3 or axis == -1:
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tf_data_format = 'NHWC'
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else:
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tf_data_format = None
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if (tf_data_format == 'NHWC' or
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tf_data_format == 'NCHW' and _has_nchw_support()):
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# The mean / var / beta / gamma tensors may be broadcasted
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# so they may have extra axes of size 1, which should be squeezed.
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if ndim(mean) > 1:
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mean = array_ops.reshape(mean, [-1])
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if ndim(var) > 1:
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var = array_ops.reshape(var, [-1])
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if beta is None:
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beta = zeros_like(mean)
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elif ndim(beta) > 1:
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beta = array_ops.reshape(beta, [-1])
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if gamma is None:
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gamma = ones_like(mean)
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elif ndim(gamma) > 1:
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gamma = array_ops.reshape(gamma, [-1])
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y, _, _ = nn.fused_batch_norm(
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x,
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gamma,
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beta,
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epsilon=epsilon,
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mean=mean,
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variance=var,
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data_format=tf_data_format,
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is_training=False
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)
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return y
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return nn.batch_normalization(x, mean, var, beta, gamma, epsilon)
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@ -2880,7 +2918,7 @@ class Function(object):
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if session_kwargs:
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raise ValueError('Some keys in session_kwargs are not supported at this '
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'time: %s', session_kwargs.keys())
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'time: %s', (session_kwargs.keys(),))
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self._callable_fn = None
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self._feed_arrays = None
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@ -3798,19 +3836,23 @@ def _preprocess_conv1d_input(x, data_format):
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return x, tf_data_format
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def _preprocess_conv2d_input(x, data_format):
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def _preprocess_conv2d_input(x, data_format, force_transpose=False):
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"""Transpose and cast the input before the conv2d.
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Arguments:
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x: input tensor.
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data_format: string, `"channels_last"` or `"channels_first"`.
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force_transpose: Boolean. If True, the input will always be transposed
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from NCHW to NHWC if `data_format` is `"channels_first"`.
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If False, the transposition only occurs on CPU (GPU ops are
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assumed to support NCHW).
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Returns:
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A tensor.
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"""
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tf_data_format = 'NHWC'
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if data_format == 'channels_first':
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if not _has_nchw_support():
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if not _has_nchw_support() or force_transpose:
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x = array_ops.transpose(x, (0, 2, 3, 1)) # NCHW -> NHWC
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else:
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tf_data_format = 'NCHW'
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@ -3958,7 +4000,8 @@ def conv2d_transpose(x,
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output_shape,
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strides=(1, 1),
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padding='valid',
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data_format=None):
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data_format=None,
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dilation_rate=(1, 1)):
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"""2D deconvolution (i.e.
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transposed convolution).
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@ -3972,6 +4015,7 @@ def conv2d_transpose(x,
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data_format: string, `"channels_last"` or `"channels_first"`.
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Whether to use Theano or TensorFlow/CNTK data format
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for inputs/kernels/outputs.
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dilation_rate: Tuple of 2 integers.
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Returns:
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A tensor, result of transposed 2D convolution.
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@ -3987,7 +4031,13 @@ def conv2d_transpose(x,
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if isinstance(output_shape, (tuple, list)):
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output_shape = array_ops.stack(output_shape)
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x, tf_data_format = _preprocess_conv2d_input(x, data_format)
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# `atrous_conv2d_transpose` only supports NHWC format, even on GPU.
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if data_format == 'channels_first' and dilation_rate != (1, 1):
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force_transpose = True
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else:
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force_transpose = False
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x, tf_data_format = _preprocess_conv2d_input(x, data_format, force_transpose)
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if data_format == 'channels_first' and tf_data_format == 'NHWC':
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output_shape = (output_shape[0], output_shape[2], output_shape[3],
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@ -4002,13 +4052,18 @@ def conv2d_transpose(x,
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else:
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strides = (1, 1) + strides
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x = nn.conv2d_transpose(
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x,
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kernel,
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output_shape,
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strides,
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padding=padding,
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data_format=tf_data_format)
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if dilation_rate == (1, 1):
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x = nn.conv2d_transpose(x, kernel, output_shape, strides,
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padding=padding,
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data_format=tf_data_format)
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else:
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assert dilation_rate[0] == dilation_rate[1]
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x = nn.atrous_conv2d_transpose(
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x,
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kernel,
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output_shape,
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rate=dilation_rate[0],
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padding=padding)
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if data_format == 'channels_first' and tf_data_format == 'NHWC':
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x = array_ops.transpose(x, (0, 3, 1, 2)) # NHWC -> NCHW
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return x
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@ -26,6 +26,7 @@ from tensorflow.python import keras
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from tensorflow.python.framework import dtypes
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from tensorflow.python.framework import ops
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from tensorflow.python.framework import sparse_tensor
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from tensorflow.python.ops import nn
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from tensorflow.python.ops import variables
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from tensorflow.python.platform import test
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from tensorflow.python.util import tf_inspect
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@ -1381,6 +1382,36 @@ class BackendNNOpsTest(test.TestCase, parameterized.TestCase):
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self.assertEqual(mean.get_shape().as_list(), [3,])
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self.assertEqual(var.get_shape().as_list(), [3,])
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def test_batch_normalization(self):
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g_val = np.random.random((3,))
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b_val = np.random.random((3,))
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gamma = keras.backend.variable(g_val)
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beta = keras.backend.variable(b_val)
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# 3D NHC case
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val = np.random.random((10, 5, 3))
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x = keras.backend.variable(val)
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mean, var = nn.moments(x, (0, 1), None, None, False)
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normed = keras.backend.batch_normalization(
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x, mean, var, beta, gamma, axis=-1, epsilon=1e-3)
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self.assertEqual(normed.shape.as_list(), [10, 5, 3])
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# 4D NHWC case
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val = np.random.random((10, 5, 5, 3))
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x = keras.backend.variable(val)
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mean, var = nn.moments(x, (0, 1, 2), None, None, False)
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normed = keras.backend.batch_normalization(
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x, mean, var, beta, gamma, axis=-1, epsilon=1e-3)
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self.assertEqual(normed.shape.as_list(), [10, 5, 5, 3])
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# 4D NCHW case
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val = np.random.random((10, 3, 5, 5))
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x = keras.backend.variable(val)
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mean, var = nn.moments(x, (0, 2, 3), None, None, False)
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normed = keras.backend.batch_normalization(
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x, mean, var, beta, gamma, axis=1, epsilon=1e-3)
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self.assertEqual(normed.shape.as_list(), [10, 3, 5, 5])
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class TestCTC(test.TestCase):
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@ -1506,12 +1537,13 @@ class TestRandomOps(test.TestCase):
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self.assertAllClose(np.min(y), -2., atol=0.1)
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def test_string_input(self):
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seq = keras.Sequential([
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keras.layers.InputLayer(input_shape=(1,), dtype=dtypes.string),
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keras.layers.Lambda(lambda x: x[0])
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])
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preds = seq.predict([['tensorflow eager']])
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self.assertEqual(preds.shape, (1,))
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with self.cached_session():
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seq = keras.Sequential([
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keras.layers.InputLayer(input_shape=(1,), dtype=dtypes.string),
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keras.layers.Lambda(lambda x: x[0])
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])
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preds = seq.predict([['tensorflow eager']])
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self.assertEqual(preds.shape, (1,))
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if __name__ == '__main__':
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test.main()
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@ -781,6 +781,10 @@ class LearningRateScheduler(Callback):
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print('\nEpoch %05d: LearningRateScheduler reducing learning '
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'rate to %s.' % (epoch + 1, lr))
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def on_epoch_end(self, epoch, logs=None):
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logs = logs or {}
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logs['lr'] = K.get_value(self.model.optimizer.lr)
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@tf_export('keras.callbacks.TensorBoard')
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class TensorBoard(Callback):
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@ -1641,10 +1641,11 @@ class Network(base_layer.Layer):
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ValueError: if `summary()` is called before the model is built.
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"""
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if not self.built:
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raise ValueError('This model has never been called, thus its weights '
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'have not yet been created, so no summary can be '
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'displayed. Build the model first '
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'(e.g. by calling it on some data).')
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raise ValueError('This model has not yet been built. '
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'Build the model first by calling `build()` or calling '
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'`fit()` with some data, or specify '
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'an `input_shape` argument in the first layer(s) for '
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'automatic build.')
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layer_utils.print_summary(self,
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line_length=line_length,
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positions=positions,
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@ -645,6 +645,14 @@ class Conv2DTranspose(Conv2D):
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Specifying any stride value != 1 is incompatible with specifying
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any `dilation_rate` value != 1.
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padding: one of `"valid"` or `"same"` (case-insensitive).
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output_padding: An integer or tuple/list of 2 integers,
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specifying the amount of padding along the height and width
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of the output tensor.
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Can be a single integer to specify the same value for all
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spatial dimensions.
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The amount of output padding along a given dimension must be
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lower than the stride along that same dimension.
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If set to `None` (default), the output shape is inferred.
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data_format: A string,
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one of `channels_last` (default) or `channels_first`.
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The ordering of the dimensions in the inputs.
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@ -700,7 +708,9 @@ class Conv2DTranspose(Conv2D):
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kernel_size,
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strides=(1, 1),
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padding='valid',
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output_padding=None,
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data_format=None,
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dilation_rate=(1, 1),
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activation=None,
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use_bias=True,
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kernel_initializer='glorot_uniform',
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@ -717,6 +727,7 @@ class Conv2DTranspose(Conv2D):
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strides=strides,
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padding=padding,
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data_format=data_format,
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dilation_rate=dilation_rate,
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activation=activations.get(activation),
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use_bias=use_bias,
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kernel_initializer=initializers.get(kernel_initializer),
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@ -728,6 +739,16 @@ class Conv2DTranspose(Conv2D):
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bias_constraint=constraints.get(bias_constraint),
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**kwargs)
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self.output_padding = output_padding
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if self.output_padding is not None:
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self.output_padding = conv_utils.normalize_tuple(
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self.output_padding, 2, 'output_padding')
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for stride, out_pad in zip(self.strides, self.output_padding):
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if out_pad >= stride:
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raise ValueError('Stride ' + str(self.strides) + ' must be '
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'greater than output padding ' +
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str(self.output_padding))
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def build(self, input_shape):
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input_shape = tensor_shape.TensorShape(input_shape)
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if len(input_shape) != 4:
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@ -769,51 +790,50 @@ class Conv2DTranspose(Conv2D):
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inputs_shape = array_ops.shape(inputs)
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batch_size = inputs_shape[0]
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if self.data_format == 'channels_first':
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c_axis, h_axis, w_axis = 1, 2, 3
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h_axis, w_axis = 2, 3
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else:
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c_axis, h_axis, w_axis = 3, 1, 2
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h_axis, w_axis = 1, 2
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height, width = inputs_shape[h_axis], inputs_shape[w_axis]
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kernel_h, kernel_w = self.kernel_size
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stride_h, stride_w = self.strides
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if self.output_padding is None:
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out_pad_h = out_pad_w = None
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else:
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out_pad_h, out_pad_w = self.output_padding
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# Infer the dynamic output shape:
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out_height = conv_utils.deconv_output_length(height,
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kernel_h,
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self.padding,
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stride_h)
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padding=self.padding,
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output_padding=out_pad_h,
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stride=stride_h,
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dilation=self.dilation_rate[0])
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out_width = conv_utils.deconv_output_length(width,
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kernel_w,
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self.padding,
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stride_w)
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padding=self.padding,
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output_padding=out_pad_w,
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stride=stride_w,
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dilation=self.dilation_rate[1])
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if self.data_format == 'channels_first':
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output_shape = (batch_size, self.filters, out_height, out_width)
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strides = (1, 1, stride_h, stride_w)
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else:
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output_shape = (batch_size, out_height, out_width, self.filters)
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strides = (1, stride_h, stride_w, 1)
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output_shape_tensor = array_ops.stack(output_shape)
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outputs = nn.conv2d_transpose(
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outputs = backend.conv2d_transpose(
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inputs,
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self.kernel,
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output_shape_tensor,
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strides,
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padding=self.padding.upper(),
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data_format=conv_utils.convert_data_format(self.data_format, ndim=4))
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strides=self.strides,
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padding=self.padding,
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data_format=self.data_format,
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dilation_rate=self.dilation_rate)
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if not context.executing_eagerly():
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# Infer the static output shape:
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out_shape = inputs.get_shape().as_list()
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out_shape[c_axis] = self.filters
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out_shape[h_axis] = conv_utils.deconv_output_length(out_shape[h_axis],
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kernel_h,
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self.padding,
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stride_h)
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out_shape[w_axis] = conv_utils.deconv_output_length(out_shape[w_axis],
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kernel_w,
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self.padding,
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stride_w)
|
||||
out_shape = self.compute_output_shape(inputs.shape)
|
||||
outputs.set_shape(out_shape)
|
||||
|
||||
if self.use_bias:
|
||||
@ -837,13 +857,33 @@ class Conv2DTranspose(Conv2D):
|
||||
kernel_h, kernel_w = self.kernel_size
|
||||
stride_h, stride_w = self.strides
|
||||
|
||||
if self.output_padding is None:
|
||||
out_pad_h = out_pad_w = None
|
||||
else:
|
||||
out_pad_h, out_pad_w = self.output_padding
|
||||
|
||||
output_shape[c_axis] = self.filters
|
||||
output_shape[h_axis] = conv_utils.deconv_output_length(
|
||||
output_shape[h_axis], kernel_h, self.padding, stride_h)
|
||||
output_shape[h_axis],
|
||||
kernel_h,
|
||||
padding=self.padding,
|
||||
output_padding=out_pad_h,
|
||||
stride=stride_h,
|
||||
dilation=self.dilation_rate[0])
|
||||
output_shape[w_axis] = conv_utils.deconv_output_length(
|
||||
output_shape[w_axis], kernel_w, self.padding, stride_w)
|
||||
output_shape[w_axis],
|
||||
kernel_w,
|
||||
padding=self.padding,
|
||||
output_padding=out_pad_w,
|
||||
stride=stride_w,
|
||||
dilation=self.dilation_rate[1])
|
||||
return tensor_shape.TensorShape(output_shape)
|
||||
|
||||
def get_config(self):
|
||||
config = super(Conv2DTranspose, self).get_config()
|
||||
config['output_padding'] = self.output_padding
|
||||
return config
|
||||
|
||||
|
||||
@tf_export('keras.layers.Conv3DTranspose',
|
||||
'keras.layers.Convolution3DTranspose')
|
||||
@ -878,6 +918,14 @@ class Conv3DTranspose(Conv3D):
|
||||
Specifying any stride value != 1 is incompatible with specifying
|
||||
any `dilation_rate` value != 1.
|
||||
padding: one of `"valid"` or `"same"` (case-insensitive).
|
||||
output_padding: An integer or tuple/list of 3 integers,
|
||||
specifying the amount of padding along the depth, height, and
|
||||
width.
|
||||
Can be a single integer to specify the same value for all
|
||||
spatial dimensions.
|
||||
The amount of output padding along a given dimension must be
|
||||
lower than the stride along that same dimension.
|
||||
If set to `None` (default), the output shape is inferred.
|
||||
data_format: A string,
|
||||
one of `channels_last` (default) or `channels_first`.
|
||||
The ordering of the dimensions in the inputs.
|
||||
@ -943,6 +991,7 @@ class Conv3DTranspose(Conv3D):
|
||||
kernel_size,
|
||||
strides=(1, 1, 1),
|
||||
padding='valid',
|
||||
output_padding=None,
|
||||
data_format=None,
|
||||
activation=None,
|
||||
use_bias=True,
|
||||
@ -971,6 +1020,16 @@ class Conv3DTranspose(Conv3D):
|
||||
bias_constraint=constraints.get(bias_constraint),
|
||||
**kwargs)
|
||||
|
||||
self.output_padding = output_padding
|
||||
if self.output_padding is not None:
|
||||
self.output_padding = conv_utils.normalize_tuple(
|
||||
self.output_padding, 3, 'output_padding')
|
||||
for stride, out_pad in zip(self.strides, self.output_padding):
|
||||
if out_pad >= stride:
|
||||
raise ValueError('Stride ' + str(self.strides) + ' must be '
|
||||
'greater than output padding ' +
|
||||
str(self.output_padding))
|
||||
|
||||
def build(self, input_shape):
|
||||
input_shape = tensor_shape.TensorShape(input_shape)
|
||||
if len(input_shape) != 5:
|
||||
@ -1012,11 +1071,9 @@ class Conv3DTranspose(Conv3D):
|
||||
inputs_shape = array_ops.shape(inputs)
|
||||
batch_size = inputs_shape[0]
|
||||
if self.data_format == 'channels_first':
|
||||
c_axis, d_axis, h_axis, w_axis = 1, 2, 3, 4
|
||||
d_axis, h_axis, w_axis = 2, 3, 4
|
||||
else:
|
||||
c_axis, d_axis, h_axis, w_axis = 4, 1, 2, 3
|
||||
|
||||
self.input_spec = InputSpec(ndim=5, axes={c_axis: inputs_shape[c_axis]})
|
||||
d_axis, h_axis, w_axis = 1, 2, 3
|
||||
|
||||
depth = inputs_shape[d_axis]
|
||||
height = inputs_shape[h_axis]
|
||||
@ -1025,19 +1082,27 @@ class Conv3DTranspose(Conv3D):
|
||||
kernel_d, kernel_h, kernel_w = self.kernel_size
|
||||
stride_d, stride_h, stride_w = self.strides
|
||||
|
||||
if self.output_padding is None:
|
||||
out_pad_d = out_pad_h = out_pad_w = None
|
||||
else:
|
||||
out_pad_d, out_pad_h, out_pad_w = self.output_padding
|
||||
|
||||
# Infer the dynamic output shape:
|
||||
out_depth = conv_utils.deconv_output_length(depth,
|
||||
kernel_d,
|
||||
self.padding,
|
||||
stride_d)
|
||||
padding=self.padding,
|
||||
output_padding=out_pad_d,
|
||||
stride=stride_d)
|
||||
out_height = conv_utils.deconv_output_length(height,
|
||||
kernel_h,
|
||||
self.padding,
|
||||
stride_h)
|
||||
padding=self.padding,
|
||||
output_padding=out_pad_h,
|
||||
stride=stride_h)
|
||||
out_width = conv_utils.deconv_output_length(width,
|
||||
kernel_w,
|
||||
self.padding,
|
||||
stride_w)
|
||||
padding=self.padding,
|
||||
output_padding=out_pad_w,
|
||||
stride=stride_w)
|
||||
if self.data_format == 'channels_first':
|
||||
output_shape = (batch_size, self.filters, out_depth, out_height,
|
||||
out_width)
|
||||
@ -1058,20 +1123,7 @@ class Conv3DTranspose(Conv3D):
|
||||
|
||||
if not context.executing_eagerly():
|
||||
# Infer the static output shape:
|
||||
out_shape = inputs.get_shape().as_list()
|
||||
out_shape[c_axis] = self.filters
|
||||
out_shape[d_axis] = conv_utils.deconv_output_length(out_shape[d_axis],
|
||||
kernel_d,
|
||||
self.padding,
|
||||
stride_d)
|
||||
out_shape[h_axis] = conv_utils.deconv_output_length(out_shape[h_axis],
|
||||
kernel_h,
|
||||
self.padding,
|
||||
stride_h)
|
||||
out_shape[w_axis] = conv_utils.deconv_output_length(out_shape[w_axis],
|
||||
kernel_w,
|
||||
self.padding,
|
||||
stride_w)
|
||||
out_shape = self.compute_output_shape(inputs.shape)
|
||||
outputs.set_shape(out_shape)
|
||||
|
||||
if self.use_bias:
|
||||
@ -1109,15 +1161,38 @@ class Conv3DTranspose(Conv3D):
|
||||
kernel_d, kernel_h, kernel_w = self.kernel_size
|
||||
stride_d, stride_h, stride_w = self.strides
|
||||
|
||||
if self.output_padding is None:
|
||||
out_pad_d = out_pad_h = out_pad_w = None
|
||||
else:
|
||||
out_pad_d, out_pad_h, out_pad_w = self.output_padding
|
||||
|
||||
output_shape[c_axis] = self.filters
|
||||
output_shape[d_axis] = conv_utils.deconv_output_length(
|
||||
output_shape[d_axis], kernel_d, self.padding, stride_d)
|
||||
output_shape[d_axis],
|
||||
kernel_d,
|
||||
padding=self.padding,
|
||||
output_padding=out_pad_d,
|
||||
stride=stride_d)
|
||||
output_shape[h_axis] = conv_utils.deconv_output_length(
|
||||
output_shape[h_axis], kernel_h, self.padding, stride_h)
|
||||
output_shape[h_axis],
|
||||
kernel_h,
|
||||
padding=self.padding,
|
||||
output_padding=out_pad_h,
|
||||
stride=stride_h)
|
||||
output_shape[w_axis] = conv_utils.deconv_output_length(
|
||||
output_shape[w_axis], kernel_w, self.padding, stride_w)
|
||||
output_shape[w_axis],
|
||||
kernel_w,
|
||||
padding=self.padding,
|
||||
output_padding=out_pad_w,
|
||||
stride=stride_w)
|
||||
return tensor_shape.TensorShape(output_shape)
|
||||
|
||||
def get_config(self):
|
||||
config = super(Conv3DTranspose, self).get_config()
|
||||
config.pop('dilation_rate')
|
||||
config['output_padding'] = self.output_padding
|
||||
return config
|
||||
|
||||
|
||||
class SeparableConv(Conv):
|
||||
"""Abstract base layer for separable nD convolution.
|
||||
|
||||
@ -204,6 +204,9 @@ class Conv2DTransposeTest(test.TestCase):
|
||||
if test.is_gpu_available(cuda_only=True):
|
||||
self._run_test(kwargs, 'data_format', ['channels_first'])
|
||||
|
||||
kwargs['strides'] = (2, 2)
|
||||
self._run_test(kwargs, 'output_padding', [(1, 1)])
|
||||
|
||||
def test_conv2dtranspose_regularizers(self):
|
||||
kwargs = {
|
||||
'filters': 3,
|
||||
@ -239,6 +242,31 @@ class Conv2DTransposeTest(test.TestCase):
|
||||
self.assertEqual(layer.kernel.constraint, k_constraint)
|
||||
self.assertEqual(layer.bias.constraint, b_constraint)
|
||||
|
||||
@tf_test_util.run_in_graph_and_eager_modes
|
||||
def test_conv2d_transpose_dilation(self):
|
||||
testing_utils.layer_test(keras.layers.Conv2DTranspose,
|
||||
kwargs={'filters': 2,
|
||||
'kernel_size': 3,
|
||||
'padding': 'same',
|
||||
'data_format': 'channels_last',
|
||||
'dilation_rate': (2, 2)},
|
||||
input_shape=(2, 5, 6, 3))
|
||||
|
||||
input_data = np.arange(48).reshape((1, 4, 4, 3)).astype(np.float32)
|
||||
expected_output = np.float32([[192, 228, 192, 228],
|
||||
[336, 372, 336, 372],
|
||||
[192, 228, 192, 228],
|
||||
[336, 372, 336, 372]]).reshape((1, 4, 4, 1))
|
||||
testing_utils.layer_test(keras.layers.Conv2DTranspose,
|
||||
input_data=input_data,
|
||||
kwargs={'filters': 1,
|
||||
'kernel_size': 3,
|
||||
'padding': 'same',
|
||||
'data_format': 'channels_last',
|
||||
'dilation_rate': (2, 2),
|
||||
'kernel_initializer': 'ones'},
|
||||
expected_output=expected_output)
|
||||
|
||||
|
||||
class Conv3DTransposeTest(test.TestCase):
|
||||
|
||||
@ -270,6 +298,9 @@ class Conv3DTransposeTest(test.TestCase):
|
||||
if test.is_gpu_available(cuda_only=True):
|
||||
self._run_test(kwargs, 'data_format', ['channels_first'])
|
||||
|
||||
kwargs['strides'] = (2, 2, 2)
|
||||
self._run_test(kwargs, 'output_padding', [(1, 1, 1)])
|
||||
|
||||
def test_conv3dtranspose_regularizers(self):
|
||||
kwargs = {
|
||||
'filters': 3,
|
||||
|
||||
@ -18,12 +18,15 @@ from __future__ import absolute_import
|
||||
from __future__ import division
|
||||
from __future__ import print_function
|
||||
|
||||
import functools
|
||||
|
||||
from tensorflow.python.framework import tensor_shape
|
||||
from tensorflow.python.keras import backend
|
||||
from tensorflow.python.keras.engine.base_layer import InputSpec
|
||||
from tensorflow.python.keras.engine.base_layer import Layer
|
||||
from tensorflow.python.keras.utils import conv_utils
|
||||
from tensorflow.python.ops import array_ops
|
||||
from tensorflow.python.ops import math_ops
|
||||
from tensorflow.python.ops import nn
|
||||
from tensorflow.python.util.tf_export import tf_export
|
||||
|
||||
@ -41,16 +44,18 @@ class Pooling1D(Layer):
|
||||
strides of the pooling operation.
|
||||
padding: A string. The padding method, either 'valid' or 'same'.
|
||||
Case-insensitive.
|
||||
data_format: A string, one of `channels_last` (default) or `channels_first`.
|
||||
data_format: A string,
|
||||
one of `channels_last` (default) or `channels_first`.
|
||||
The ordering of the dimensions in the inputs.
|
||||
`channels_last` corresponds to inputs with shape
|
||||
`(batch, length, channels)` while `channels_first` corresponds to
|
||||
inputs with shape `(batch, channels, length)`.
|
||||
`(batch, steps, features)` while `channels_first`
|
||||
corresponds to inputs with shape
|
||||
`(batch, features, steps)`.
|
||||
name: A string, the name of the layer.
|
||||
"""
|
||||
|
||||
def __init__(self, pool_function, pool_size, strides,
|
||||
padding='valid', data_format=None,
|
||||
padding='valid', data_format='channels_last',
|
||||
name=None, **kwargs):
|
||||
super(Pooling1D, self).__init__(name=name, **kwargs)
|
||||
if data_format is None:
|
||||
@ -65,45 +70,39 @@ class Pooling1D(Layer):
|
||||
self.input_spec = InputSpec(ndim=3)
|
||||
|
||||
def call(self, inputs):
|
||||
# There is no TF op for 1D pooling, hence we make the inputs 4D.
|
||||
if self.data_format == 'channels_last':
|
||||
# input is NWC, make it NHWC
|
||||
inputs = array_ops.expand_dims(inputs, 1)
|
||||
# pool on the W dim
|
||||
pool_shape = (1, 1) + self.pool_size + (1,)
|
||||
strides = (1, 1) + self.strides + (1,)
|
||||
data_format = 'NHWC'
|
||||
else:
|
||||
# input is NCW, make it NCHW
|
||||
inputs = array_ops.expand_dims(inputs, 2)
|
||||
# pool on the W dim
|
||||
pool_shape = (1, 1, 1) + self.pool_size
|
||||
strides = (1, 1, 1) + self.strides
|
||||
data_format = 'NCHW'
|
||||
|
||||
pad_axis = 2 if self.data_format == 'channels_last' else 3
|
||||
inputs = array_ops.expand_dims(inputs, pad_axis)
|
||||
outputs = self.pool_function(
|
||||
inputs,
|
||||
ksize=pool_shape,
|
||||
strides=strides,
|
||||
padding=self.padding.upper(),
|
||||
data_format=data_format)
|
||||
|
||||
if self.data_format == 'channels_last':
|
||||
return array_ops.squeeze(outputs, 1)
|
||||
else:
|
||||
return array_ops.squeeze(outputs, 2)
|
||||
self.pool_size + (1,),
|
||||
strides=self.strides + (1,),
|
||||
padding=self.padding,
|
||||
data_format=self.data_format)
|
||||
return array_ops.squeeze(outputs, pad_axis)
|
||||
|
||||
def compute_output_shape(self, input_shape):
|
||||
input_shape = tensor_shape.TensorShape(input_shape).as_list()
|
||||
length = conv_utils.conv_output_length(input_shape[1], self.pool_size[0],
|
||||
self.padding, self.strides[0])
|
||||
return tensor_shape.TensorShape([input_shape[0], length, input_shape[2]])
|
||||
if self.data_format == 'channels_first':
|
||||
steps = input_shape[2]
|
||||
features = input_shape[1]
|
||||
else:
|
||||
steps = input_shape[1]
|
||||
features = input_shape[2]
|
||||
length = conv_utils.conv_output_length(steps,
|
||||
self.pool_size[0],
|
||||
self.padding,
|
||||
self.strides[0])
|
||||
if self.data_format == 'channels_first':
|
||||
return tensor_shape.TensorShape([input_shape[0], features, length])
|
||||
else:
|
||||
return tensor_shape.TensorShape([input_shape[0], length, features])
|
||||
|
||||
def get_config(self):
|
||||
config = {
|
||||
'strides': self.strides,
|
||||
'pool_size': self.pool_size,
|
||||
'padding': self.padding
|
||||
'padding': self.padding,
|
||||
'data_format': self.data_format,
|
||||
}
|
||||
base_config = super(Pooling1D, self).get_config()
|
||||
return dict(list(base_config.items()) + list(config.items()))
|
||||
@ -119,19 +118,36 @@ class MaxPooling1D(Pooling1D):
|
||||
E.g. 2 will halve the input.
|
||||
If None, it will default to `pool_size`.
|
||||
padding: One of `"valid"` or `"same"` (case-insensitive).
|
||||
data_format: A string,
|
||||
one of `channels_last` (default) or `channels_first`.
|
||||
The ordering of the dimensions in the inputs.
|
||||
`channels_last` corresponds to inputs with shape
|
||||
`(batch, steps, features)` while `channels_first`
|
||||
corresponds to inputs with shape
|
||||
`(batch, features, steps)`.
|
||||
|
||||
Input shape:
|
||||
3D tensor with shape: `(batch_size, steps, features)`.
|
||||
- If `data_format='channels_last'`:
|
||||
3D tensor with shape:
|
||||
`(batch_size, steps, features)`
|
||||
- If `data_format='channels_first'`:
|
||||
3D tensor with shape:
|
||||
`(batch_size, features, steps)`
|
||||
|
||||
Output shape:
|
||||
3D tensor with shape: `(batch_size, downsampled_steps, features)`.
|
||||
- If `data_format='channels_last'`:
|
||||
3D tensor with shape:
|
||||
`(batch_size, downsampled_steps, features)`
|
||||
- If `data_format='channels_first'`:
|
||||
3D tensor with shape:
|
||||
`(batch_size, features, downsampled_steps)`
|
||||
"""
|
||||
|
||||
def __init__(self, pool_size=2, strides=None,
|
||||
padding='valid', data_format=None, **kwargs):
|
||||
padding='valid', data_format='channels_last', **kwargs):
|
||||
|
||||
super(MaxPooling1D, self).__init__(
|
||||
nn.max_pool,
|
||||
functools.partial(backend.pool2d, pool_mode='max'),
|
||||
pool_size=pool_size,
|
||||
strides=strides,
|
||||
padding=padding,
|
||||
@ -149,18 +165,35 @@ class AveragePooling1D(Pooling1D):
|
||||
E.g. 2 will halve the input.
|
||||
If None, it will default to `pool_size`.
|
||||
padding: One of `"valid"` or `"same"` (case-insensitive).
|
||||
data_format: A string,
|
||||
one of `channels_last` (default) or `channels_first`.
|
||||
The ordering of the dimensions in the inputs.
|
||||
`channels_last` corresponds to inputs with shape
|
||||
`(batch, steps, features)` while `channels_first`
|
||||
corresponds to inputs with shape
|
||||
`(batch, features, steps)`.
|
||||
|
||||
Input shape:
|
||||
3D tensor with shape: `(batch_size, steps, features)`.
|
||||
- If `data_format='channels_last'`:
|
||||
3D tensor with shape:
|
||||
`(batch_size, steps, features)`
|
||||
- If `data_format='channels_first'`:
|
||||
3D tensor with shape:
|
||||
`(batch_size, features, steps)`
|
||||
|
||||
Output shape:
|
||||
3D tensor with shape: `(batch_size, downsampled_steps, features)`.
|
||||
- If `data_format='channels_last'`:
|
||||
3D tensor with shape:
|
||||
`(batch_size, downsampled_steps, features)`
|
||||
- If `data_format='channels_first'`:
|
||||
3D tensor with shape:
|
||||
`(batch_size, features, downsampled_steps)`
|
||||
"""
|
||||
|
||||
def __init__(self, pool_size=2, strides=None,
|
||||
padding='valid', data_format=None, **kwargs):
|
||||
padding='valid', data_format='channels_last', **kwargs):
|
||||
super(AveragePooling1D, self).__init__(
|
||||
nn.avg_pool,
|
||||
functools.partial(backend.pool2d, pool_mode='avg'),
|
||||
pool_size=pool_size,
|
||||
strides=strides,
|
||||
padding=padding,
|
||||
@ -561,41 +594,96 @@ class GlobalPooling1D(Layer):
|
||||
"""Abstract class for different global pooling 1D layers.
|
||||
"""
|
||||
|
||||
def __init__(self, **kwargs):
|
||||
def __init__(self, data_format='channels_last', **kwargs):
|
||||
super(GlobalPooling1D, self).__init__(**kwargs)
|
||||
self.input_spec = InputSpec(ndim=3)
|
||||
self.data_format = conv_utils.normalize_data_format(data_format)
|
||||
|
||||
def compute_output_shape(self, input_shape):
|
||||
input_shape = tensor_shape.TensorShape(input_shape).as_list()
|
||||
return tensor_shape.TensorShape([input_shape[0], input_shape[2]])
|
||||
if self.data_format == 'channels_first':
|
||||
return tensor_shape.TensorShape([input_shape[0], input_shape[1]])
|
||||
else:
|
||||
return tensor_shape.TensorShape([input_shape[0], input_shape[2]])
|
||||
|
||||
def call(self, inputs):
|
||||
raise NotImplementedError
|
||||
|
||||
def get_config(self):
|
||||
config = {'data_format': self.data_format}
|
||||
base_config = super(GlobalPooling1D, self).get_config()
|
||||
return dict(list(base_config.items()) + list(config.items()))
|
||||
|
||||
|
||||
@tf_export('keras.layers.GlobalAveragePooling1D',
|
||||
'keras.layers.GlobalAvgPool1D')
|
||||
class GlobalAveragePooling1D(GlobalPooling1D):
|
||||
"""Global average pooling operation for temporal data.
|
||||
|
||||
Arguments:
|
||||
data_format: A string,
|
||||
one of `channels_last` (default) or `channels_first`.
|
||||
The ordering of the dimensions in the inputs.
|
||||
`channels_last` corresponds to inputs with shape
|
||||
`(batch, steps, features)` while `channels_first`
|
||||
corresponds to inputs with shape
|
||||
`(batch, features, steps)`.
|
||||
|
||||
Input shape:
|
||||
3D tensor with shape: `(batch_size, steps, features)`.
|
||||
- If `data_format='channels_last'`:
|
||||
3D tensor with shape:
|
||||
`(batch_size, steps, features)`
|
||||
- If `data_format='channels_first'`:
|
||||
3D tensor with shape:
|
||||
`(batch_size, features, steps)`
|
||||
|
||||
Output shape:
|
||||
2D tensor with shape:
|
||||
`(batch_size, features)`
|
||||
"""
|
||||
|
||||
def call(self, inputs):
|
||||
return backend.mean(inputs, axis=1)
|
||||
def __init__(self, data_format='channels_last', **kwargs):
|
||||
super(GlobalAveragePooling1D, self).__init__(data_format=data_format,
|
||||
**kwargs)
|
||||
self.supports_masking = True
|
||||
|
||||
def call(self, inputs, mask=None):
|
||||
steps_axis = 1 if self.data_format == 'channels_last' else 2
|
||||
if mask is not None:
|
||||
mask = math_ops.cast(mask, backend.floatx())
|
||||
input_shape = inputs.shape.as_list()
|
||||
broadcast_shape = [-1, input_shape[steps_axis], 1]
|
||||
mask = array_ops.reshape(mask, broadcast_shape)
|
||||
inputs *= mask
|
||||
return backend.sum(inputs, axis=steps_axis) / math_ops.reduce_sum(
|
||||
mask, axis=steps_axis)
|
||||
else:
|
||||
return backend.mean(inputs, axis=steps_axis)
|
||||
|
||||
def compute_mask(self, inputs, mask=None):
|
||||
return None
|
||||
|
||||
|
||||
@tf_export('keras.layers.GlobalMaxPool1D', 'keras.layers.GlobalMaxPooling1D')
|
||||
class GlobalMaxPooling1D(GlobalPooling1D):
|
||||
"""Global max pooling operation for temporal data.
|
||||
|
||||
Arguments:
|
||||
data_format: A string,
|
||||
one of `channels_last` (default) or `channels_first`.
|
||||
The ordering of the dimensions in the inputs.
|
||||
`channels_last` corresponds to inputs with shape
|
||||
`(batch, steps, features)` while `channels_first`
|
||||
corresponds to inputs with shape
|
||||
`(batch, features, steps)`.
|
||||
|
||||
Input shape:
|
||||
3D tensor with shape: `(batch_size, steps, features)`.
|
||||
- If `data_format='channels_last'`:
|
||||
3D tensor with shape:
|
||||
`(batch_size, steps, features)`
|
||||
- If `data_format='channels_first'`:
|
||||
3D tensor with shape:
|
||||
`(batch_size, features, steps)`
|
||||
|
||||
Output shape:
|
||||
2D tensor with shape:
|
||||
@ -603,7 +691,8 @@ class GlobalMaxPooling1D(GlobalPooling1D):
|
||||
"""
|
||||
|
||||
def call(self, inputs):
|
||||
return backend.max(inputs, axis=1)
|
||||
steps_axis = 1 if self.data_format == 'channels_last' else 2
|
||||
return backend.max(inputs, axis=steps_axis)
|
||||
|
||||
|
||||
class GlobalPooling2D(Layer):
|
||||
|
||||
@ -18,11 +18,14 @@ from __future__ import absolute_import
|
||||
from __future__ import division
|
||||
from __future__ import print_function
|
||||
|
||||
import numpy as np
|
||||
|
||||
from tensorflow.python import keras
|
||||
from tensorflow.python.eager import context
|
||||
from tensorflow.python.framework import test_util as tf_test_util
|
||||
from tensorflow.python.keras import testing_utils
|
||||
from tensorflow.python.platform import test
|
||||
from tensorflow.python.training import rmsprop
|
||||
|
||||
|
||||
class GlobalPoolingTest(test.TestCase):
|
||||
@ -31,8 +34,26 @@ class GlobalPoolingTest(test.TestCase):
|
||||
def test_globalpooling_1d(self):
|
||||
testing_utils.layer_test(keras.layers.pooling.GlobalMaxPooling1D,
|
||||
input_shape=(3, 4, 5))
|
||||
testing_utils.layer_test(keras.layers.pooling.GlobalMaxPooling1D,
|
||||
kwargs={'data_format': 'channels_first'},
|
||||
input_shape=(3, 4, 5))
|
||||
testing_utils.layer_test(
|
||||
keras.layers.pooling.GlobalAveragePooling1D, input_shape=(3, 4, 5))
|
||||
testing_utils.layer_test(keras.layers.pooling.GlobalAveragePooling1D,
|
||||
kwargs={'data_format': 'channels_first'},
|
||||
input_shape=(3, 4, 5))
|
||||
|
||||
@tf_test_util.run_in_graph_and_eager_modes
|
||||
def test_globalpooling_1d_masking_support(self):
|
||||
model = keras.Sequential()
|
||||
model.add(keras.layers.Masking(mask_value=0., input_shape=(3, 4)))
|
||||
model.add(keras.layers.GlobalAveragePooling1D())
|
||||
model.compile(loss='mae', optimizer=rmsprop.RMSPropOptimizer(0.001))
|
||||
|
||||
model_input = np.random.random((2, 3, 4))
|
||||
model_input[0, 1:, :] = 0
|
||||
output = model.predict(model_input)
|
||||
self.assertAllClose(output[0], model_input[0, 0, :])
|
||||
|
||||
@tf_test_util.run_in_graph_and_eager_modes
|
||||
def test_globalpooling_2d(self):
|
||||
@ -172,6 +193,10 @@ class Pooling1DTest(test.TestCase):
|
||||
kwargs={'strides': stride,
|
||||
'padding': padding},
|
||||
input_shape=(3, 5, 4))
|
||||
testing_utils.layer_test(
|
||||
keras.layers.MaxPooling1D,
|
||||
kwargs={'data_format': 'channels_first'},
|
||||
input_shape=(3, 2, 6))
|
||||
|
||||
@tf_test_util.run_in_graph_and_eager_modes
|
||||
def test_averagepooling_1d(self):
|
||||
@ -183,6 +208,11 @@ class Pooling1DTest(test.TestCase):
|
||||
'padding': padding},
|
||||
input_shape=(3, 5, 4))
|
||||
|
||||
testing_utils.layer_test(
|
||||
keras.layers.AveragePooling1D,
|
||||
kwargs={'data_format': 'channels_first'},
|
||||
input_shape=(3, 2, 6))
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
test.main()
|
||||
|
||||
@ -587,6 +587,9 @@ class Bidirectional(Wrapper):
|
||||
output = y * y_rev
|
||||
elif self.merge_mode is None:
|
||||
output = [y, y_rev]
|
||||
else:
|
||||
raise ValueError(
|
||||
'Unrecognized value for `merge_mode`: %s' % (self.merge_mode))
|
||||
|
||||
# Properly set learning phase
|
||||
if (getattr(y, '_uses_learning_phase', False) or
|
||||
|
||||
@ -166,8 +166,9 @@ def layer_test(layer_cls, kwargs=None, input_shape=None, input_dtype=None,
|
||||
if expected_dim is not None:
|
||||
if expected_dim != actual_dim:
|
||||
raise AssertionError(
|
||||
'When testing layer %s, for input %s, found output_shape='
|
||||
'%s but expected to find %s.\nFull kwargs: %s' %
|
||||
'When testing layer %s **after deserialization**, '
|
||||
'for input %s, found output_shape='
|
||||
'%s but expected to find inferred shape %s.\nFull kwargs: %s' %
|
||||
(layer_cls.__name__,
|
||||
x,
|
||||
actual_output_shape,
|
||||
|
||||
@ -137,26 +137,49 @@ def conv_input_length(output_length, filter_size, padding, stride):
|
||||
return (output_length - 1) * stride - 2 * pad + filter_size
|
||||
|
||||
|
||||
def deconv_output_length(input_length, filter_size, padding, stride):
|
||||
def deconv_output_length(input_length, filter_size, padding,
|
||||
output_padding=None, stride=0, dilation=1):
|
||||
"""Determines output length of a transposed convolution given input length.
|
||||
|
||||
Arguments:
|
||||
input_length: integer.
|
||||
filter_size: integer.
|
||||
padding: one of "same", "valid", "full".
|
||||
stride: integer.
|
||||
input_length: Integer.
|
||||
filter_size: Integer.
|
||||
padding: one of `"same"`, `"valid"`, `"full"`.
|
||||
output_padding: Integer, amount of padding along the output dimension.
|
||||
Can be set to `None` in which case the output length is inferred.
|
||||
stride: Integer.
|
||||
dilation: Integer.
|
||||
|
||||
Returns:
|
||||
The output length (integer).
|
||||
"""
|
||||
assert padding in {'same', 'valid', 'full'}
|
||||
if input_length is None:
|
||||
return None
|
||||
input_length *= stride
|
||||
if padding == 'valid':
|
||||
input_length += max(filter_size - stride, 0)
|
||||
elif padding == 'full':
|
||||
input_length -= (stride + filter_size - 2)
|
||||
return input_length
|
||||
|
||||
# Get the dilated kernel size
|
||||
filter_size = filter_size + (filter_size - 1) * (dilation - 1)
|
||||
|
||||
# Infer length if output padding is None, else compute the exact length
|
||||
if output_padding is None:
|
||||
if padding == 'valid':
|
||||
length = input_length * stride + max(filter_size - stride, 0)
|
||||
elif padding == 'full':
|
||||
length = input_length * stride - (stride + filter_size - 2)
|
||||
elif padding == 'same':
|
||||
length = input_length * stride
|
||||
|
||||
else:
|
||||
if padding == 'same':
|
||||
pad = filter_size // 2
|
||||
elif padding == 'valid':
|
||||
pad = 0
|
||||
elif padding == 'full':
|
||||
pad = filter_size - 1
|
||||
|
||||
length = ((input_length - 1) * stride + filter_size - 2 * pad +
|
||||
output_padding)
|
||||
return length
|
||||
|
||||
|
||||
def normalize_data_format(value):
|
||||
|
||||
@ -244,9 +244,24 @@ def multi_gpu_model(model, gpus, cpu_merge=True, cpu_relocation=False):
|
||||
for o in range(len(outputs)):
|
||||
all_outputs[o].append(outputs[o])
|
||||
|
||||
# Deduplicate output names to handle Siamese networks.
|
||||
occurrences = {}
|
||||
for n in model.output_names:
|
||||
if n not in occurrences:
|
||||
occurrences[n] = 1
|
||||
else:
|
||||
occurrences[n] += 1
|
||||
conflict_counter = {n: 0 for n, count in occurrences.items() if count > 1}
|
||||
output_names = []
|
||||
for n in model.output_names:
|
||||
if n in conflict_counter:
|
||||
conflict_counter[n] += 1
|
||||
n += '_%d' % conflict_counter[n]
|
||||
output_names.append(n)
|
||||
|
||||
# Merge outputs under expected scope.
|
||||
with ops.device('/cpu:0' if cpu_merge else '/gpu:%d' % target_gpu_ids[0]):
|
||||
merged = []
|
||||
for name, outputs in zip(model.output_names, all_outputs):
|
||||
for name, outputs in zip(output_names, all_outputs):
|
||||
merged.append(concatenate(outputs, axis=0, name=name))
|
||||
return Model(model.inputs, merged)
|
||||
|
||||
@ -198,5 +198,31 @@ class TestMultiGPUModel(test.TestCase):
|
||||
parallel_model.compile(loss='mean_squared_error', optimizer='adam')
|
||||
parallel_model.train_on_batch(x, y)
|
||||
|
||||
def test_multi_gpu_with_siamese_network(self):
|
||||
gpus = 2
|
||||
|
||||
if not check_if_compatible_devices(gpus=gpus):
|
||||
return
|
||||
|
||||
with self.cached_session():
|
||||
input_shape = (3,)
|
||||
nested_model = keras.models.Sequential([
|
||||
keras.layers.Dense(32, input_shape=input_shape),
|
||||
keras.layers.Dense(1)
|
||||
], name='nested')
|
||||
|
||||
input1 = keras.Input(input_shape)
|
||||
input2 = keras.Input(input_shape)
|
||||
score1 = nested_model(input1)
|
||||
score2 = nested_model(input2)
|
||||
score_sum = keras.layers.Add(name='add')([score1, score2])
|
||||
|
||||
siamese = keras.models.Model(inputs=[input1, input2],
|
||||
outputs=[score_sum, score1, score2],
|
||||
name='siamese')
|
||||
parallel_siamese = keras.utils.multi_gpu_model(siamese, gpus)
|
||||
self.assertEqual(parallel_siamese.output_names,
|
||||
['add', 'nested_1', 'nested_2'])
|
||||
|
||||
if __name__ == '__main__':
|
||||
test.main()
|
||||
|
||||
@ -22,7 +22,7 @@ from tensorflow.python.util.tf_export import tf_export
|
||||
|
||||
|
||||
@tf_export('keras.utils.to_categorical')
|
||||
def to_categorical(y, num_classes=None):
|
||||
def to_categorical(y, num_classes=None, dtype='float32'):
|
||||
"""Converts a class vector (integers) to binary class matrix.
|
||||
|
||||
E.g. for use with categorical_crossentropy.
|
||||
@ -31,6 +31,7 @@ def to_categorical(y, num_classes=None):
|
||||
y: class vector to be converted into a matrix
|
||||
(integers from 0 to num_classes).
|
||||
num_classes: total number of classes.
|
||||
dtype: The data type expected by the input. Default: `'float32'`.
|
||||
|
||||
Returns:
|
||||
A binary matrix representation of the input. The classes axis is placed
|
||||
@ -44,7 +45,7 @@ def to_categorical(y, num_classes=None):
|
||||
if not num_classes:
|
||||
num_classes = np.max(y) + 1
|
||||
n = y.shape[0]
|
||||
categorical = np.zeros((n, num_classes), dtype=np.float32)
|
||||
categorical = np.zeros((n, num_classes), dtype=dtype)
|
||||
categorical[np.arange(n), y] = 1
|
||||
output_shape = input_shape + (num_classes,)
|
||||
categorical = np.reshape(categorical, output_shape)
|
||||
|
||||
@ -8,6 +8,10 @@ tf_module {
|
||||
name: "elu"
|
||||
argspec: "args=[\'x\', \'alpha\'], varargs=None, keywords=None, defaults=[\'1.0\'], "
|
||||
}
|
||||
member_method {
|
||||
name: "exponential"
|
||||
argspec: "args=[\'x\'], varargs=None, keywords=None, defaults=None"
|
||||
}
|
||||
member_method {
|
||||
name: "get"
|
||||
argspec: "args=[\'identifier\'], varargs=None, keywords=None, defaults=None"
|
||||
|
||||
@ -46,7 +46,7 @@ tf_module {
|
||||
}
|
||||
member_method {
|
||||
name: "batch_normalization"
|
||||
argspec: "args=[\'x\', \'mean\', \'var\', \'beta\', \'gamma\', \'epsilon\'], varargs=None, keywords=None, defaults=[\'0.001\'], "
|
||||
argspec: "args=[\'x\', \'mean\', \'var\', \'beta\', \'gamma\', \'axis\', \'epsilon\'], varargs=None, keywords=None, defaults=[\'-1\', \'0.001\'], "
|
||||
}
|
||||
member_method {
|
||||
name: "batch_set_value"
|
||||
@ -98,7 +98,7 @@ tf_module {
|
||||
}
|
||||
member_method {
|
||||
name: "conv2d_transpose"
|
||||
argspec: "args=[\'x\', \'kernel\', \'output_shape\', \'strides\', \'padding\', \'data_format\'], varargs=None, keywords=None, defaults=[\'(1, 1)\', \'valid\', \'None\'], "
|
||||
argspec: "args=[\'x\', \'kernel\', \'output_shape\', \'strides\', \'padding\', \'data_format\', \'dilation_rate\'], varargs=None, keywords=None, defaults=[\'(1, 1)\', \'valid\', \'None\', \'(1, 1)\'], "
|
||||
}
|
||||
member_method {
|
||||
name: "conv3d"
|
||||
|
||||
@ -83,7 +83,7 @@ tf_class {
|
||||
}
|
||||
member_method {
|
||||
name: "__init__"
|
||||
argspec: "args=[\'self\', \'pool_size\', \'strides\', \'padding\', \'data_format\'], varargs=None, keywords=kwargs, defaults=[\'2\', \'None\', \'valid\', \'None\'], "
|
||||
argspec: "args=[\'self\', \'pool_size\', \'strides\', \'padding\', \'data_format\'], varargs=None, keywords=kwargs, defaults=[\'2\', \'None\', \'valid\', \'channels_last\'], "
|
||||
}
|
||||
member_method {
|
||||
name: "add_loss"
|
||||
|
||||
@ -83,7 +83,7 @@ tf_class {
|
||||
}
|
||||
member_method {
|
||||
name: "__init__"
|
||||
argspec: "args=[\'self\', \'pool_size\', \'strides\', \'padding\', \'data_format\'], varargs=None, keywords=kwargs, defaults=[\'2\', \'None\', \'valid\', \'None\'], "
|
||||
argspec: "args=[\'self\', \'pool_size\', \'strides\', \'padding\', \'data_format\'], varargs=None, keywords=kwargs, defaults=[\'2\', \'None\', \'valid\', \'channels_last\'], "
|
||||
}
|
||||
member_method {
|
||||
name: "add_loss"
|
||||
|
||||
@ -84,7 +84,7 @@ tf_class {
|
||||
}
|
||||
member_method {
|
||||
name: "__init__"
|
||||
argspec: "args=[\'self\', \'filters\', \'kernel_size\', \'strides\', \'padding\', \'data_format\', \'activation\', \'use_bias\', \'kernel_initializer\', \'bias_initializer\', \'kernel_regularizer\', \'bias_regularizer\', \'activity_regularizer\', \'kernel_constraint\', \'bias_constraint\'], varargs=None, keywords=kwargs, defaults=[\'(1, 1)\', \'valid\', \'None\', \'None\', \'True\', \'glorot_uniform\', \'zeros\', \'None\', \'None\', \'None\', \'None\', \'None\'], "
|
||||
argspec: "args=[\'self\', \'filters\', \'kernel_size\', \'strides\', \'padding\', \'output_padding\', \'data_format\', \'dilation_rate\', \'activation\', \'use_bias\', \'kernel_initializer\', \'bias_initializer\', \'kernel_regularizer\', \'bias_regularizer\', \'activity_regularizer\', \'kernel_constraint\', \'bias_constraint\'], varargs=None, keywords=kwargs, defaults=[\'(1, 1)\', \'valid\', \'None\', \'None\', \'(1, 1)\', \'None\', \'True\', \'glorot_uniform\', \'zeros\', \'None\', \'None\', \'None\', \'None\', \'None\'], "
|
||||
}
|
||||
member_method {
|
||||
name: "add_loss"
|
||||
|
||||
@ -84,7 +84,7 @@ tf_class {
|
||||
}
|
||||
member_method {
|
||||
name: "__init__"
|
||||
argspec: "args=[\'self\', \'filters\', \'kernel_size\', \'strides\', \'padding\', \'data_format\', \'activation\', \'use_bias\', \'kernel_initializer\', \'bias_initializer\', \'kernel_regularizer\', \'bias_regularizer\', \'activity_regularizer\', \'kernel_constraint\', \'bias_constraint\'], varargs=None, keywords=kwargs, defaults=[\'(1, 1, 1)\', \'valid\', \'None\', \'None\', \'True\', \'glorot_uniform\', \'zeros\', \'None\', \'None\', \'None\', \'None\', \'None\'], "
|
||||
argspec: "args=[\'self\', \'filters\', \'kernel_size\', \'strides\', \'padding\', \'output_padding\', \'data_format\', \'activation\', \'use_bias\', \'kernel_initializer\', \'bias_initializer\', \'kernel_regularizer\', \'bias_regularizer\', \'activity_regularizer\', \'kernel_constraint\', \'bias_constraint\'], varargs=None, keywords=kwargs, defaults=[\'(1, 1, 1)\', \'valid\', \'None\', \'None\', \'None\', \'True\', \'glorot_uniform\', \'zeros\', \'None\', \'None\', \'None\', \'None\', \'None\'], "
|
||||
}
|
||||
member_method {
|
||||
name: "add_loss"
|
||||
|
||||
@ -84,7 +84,7 @@ tf_class {
|
||||
}
|
||||
member_method {
|
||||
name: "__init__"
|
||||
argspec: "args=[\'self\', \'filters\', \'kernel_size\', \'strides\', \'padding\', \'data_format\', \'activation\', \'use_bias\', \'kernel_initializer\', \'bias_initializer\', \'kernel_regularizer\', \'bias_regularizer\', \'activity_regularizer\', \'kernel_constraint\', \'bias_constraint\'], varargs=None, keywords=kwargs, defaults=[\'(1, 1)\', \'valid\', \'None\', \'None\', \'True\', \'glorot_uniform\', \'zeros\', \'None\', \'None\', \'None\', \'None\', \'None\'], "
|
||||
argspec: "args=[\'self\', \'filters\', \'kernel_size\', \'strides\', \'padding\', \'output_padding\', \'data_format\', \'dilation_rate\', \'activation\', \'use_bias\', \'kernel_initializer\', \'bias_initializer\', \'kernel_regularizer\', \'bias_regularizer\', \'activity_regularizer\', \'kernel_constraint\', \'bias_constraint\'], varargs=None, keywords=kwargs, defaults=[\'(1, 1)\', \'valid\', \'None\', \'None\', \'(1, 1)\', \'None\', \'True\', \'glorot_uniform\', \'zeros\', \'None\', \'None\', \'None\', \'None\', \'None\'], "
|
||||
}
|
||||
member_method {
|
||||
name: "add_loss"
|
||||
|
||||
@ -84,7 +84,7 @@ tf_class {
|
||||
}
|
||||
member_method {
|
||||
name: "__init__"
|
||||
argspec: "args=[\'self\', \'filters\', \'kernel_size\', \'strides\', \'padding\', \'data_format\', \'activation\', \'use_bias\', \'kernel_initializer\', \'bias_initializer\', \'kernel_regularizer\', \'bias_regularizer\', \'activity_regularizer\', \'kernel_constraint\', \'bias_constraint\'], varargs=None, keywords=kwargs, defaults=[\'(1, 1, 1)\', \'valid\', \'None\', \'None\', \'True\', \'glorot_uniform\', \'zeros\', \'None\', \'None\', \'None\', \'None\', \'None\'], "
|
||||
argspec: "args=[\'self\', \'filters\', \'kernel_size\', \'strides\', \'padding\', \'output_padding\', \'data_format\', \'activation\', \'use_bias\', \'kernel_initializer\', \'bias_initializer\', \'kernel_regularizer\', \'bias_regularizer\', \'activity_regularizer\', \'kernel_constraint\', \'bias_constraint\'], varargs=None, keywords=kwargs, defaults=[\'(1, 1, 1)\', \'valid\', \'None\', \'None\', \'None\', \'True\', \'glorot_uniform\', \'zeros\', \'None\', \'None\', \'None\', \'None\', \'None\'], "
|
||||
}
|
||||
member_method {
|
||||
name: "add_loss"
|
||||
|
||||
@ -83,7 +83,7 @@ tf_class {
|
||||
}
|
||||
member_method {
|
||||
name: "__init__"
|
||||
argspec: "args=[\'self\'], varargs=None, keywords=kwargs, defaults=None"
|
||||
argspec: "args=[\'self\', \'data_format\'], varargs=None, keywords=kwargs, defaults=[\'channels_last\'], "
|
||||
}
|
||||
member_method {
|
||||
name: "add_loss"
|
||||
@ -111,7 +111,7 @@ tf_class {
|
||||
}
|
||||
member_method {
|
||||
name: "call"
|
||||
argspec: "args=[\'self\', \'inputs\'], varargs=None, keywords=None, defaults=None"
|
||||
argspec: "args=[\'self\', \'inputs\', \'mask\'], varargs=None, keywords=None, defaults=[\'None\'], "
|
||||
}
|
||||
member_method {
|
||||
name: "compute_mask"
|
||||
|
||||
@ -83,7 +83,7 @@ tf_class {
|
||||
}
|
||||
member_method {
|
||||
name: "__init__"
|
||||
argspec: "args=[\'self\'], varargs=None, keywords=kwargs, defaults=None"
|
||||
argspec: "args=[\'self\', \'data_format\'], varargs=None, keywords=kwargs, defaults=[\'channels_last\'], "
|
||||
}
|
||||
member_method {
|
||||
name: "add_loss"
|
||||
@ -111,7 +111,7 @@ tf_class {
|
||||
}
|
||||
member_method {
|
||||
name: "call"
|
||||
argspec: "args=[\'self\', \'inputs\'], varargs=None, keywords=None, defaults=None"
|
||||
argspec: "args=[\'self\', \'inputs\', \'mask\'], varargs=None, keywords=None, defaults=[\'None\'], "
|
||||
}
|
||||
member_method {
|
||||
name: "compute_mask"
|
||||
|
||||
@ -83,7 +83,7 @@ tf_class {
|
||||
}
|
||||
member_method {
|
||||
name: "__init__"
|
||||
argspec: "args=[\'self\'], varargs=None, keywords=kwargs, defaults=None"
|
||||
argspec: "args=[\'self\', \'data_format\'], varargs=None, keywords=kwargs, defaults=[\'channels_last\'], "
|
||||
}
|
||||
member_method {
|
||||
name: "add_loss"
|
||||
|
||||
@ -83,7 +83,7 @@ tf_class {
|
||||
}
|
||||
member_method {
|
||||
name: "__init__"
|
||||
argspec: "args=[\'self\'], varargs=None, keywords=kwargs, defaults=None"
|
||||
argspec: "args=[\'self\', \'data_format\'], varargs=None, keywords=kwargs, defaults=[\'channels_last\'], "
|
||||
}
|
||||
member_method {
|
||||
name: "add_loss"
|
||||
|
||||
@ -83,7 +83,7 @@ tf_class {
|
||||
}
|
||||
member_method {
|
||||
name: "__init__"
|
||||
argspec: "args=[\'self\', \'pool_size\', \'strides\', \'padding\', \'data_format\'], varargs=None, keywords=kwargs, defaults=[\'2\', \'None\', \'valid\', \'None\'], "
|
||||
argspec: "args=[\'self\', \'pool_size\', \'strides\', \'padding\', \'data_format\'], varargs=None, keywords=kwargs, defaults=[\'2\', \'None\', \'valid\', \'channels_last\'], "
|
||||
}
|
||||
member_method {
|
||||
name: "add_loss"
|
||||
|
||||
@ -83,7 +83,7 @@ tf_class {
|
||||
}
|
||||
member_method {
|
||||
name: "__init__"
|
||||
argspec: "args=[\'self\', \'pool_size\', \'strides\', \'padding\', \'data_format\'], varargs=None, keywords=kwargs, defaults=[\'2\', \'None\', \'valid\', \'None\'], "
|
||||
argspec: "args=[\'self\', \'pool_size\', \'strides\', \'padding\', \'data_format\'], varargs=None, keywords=kwargs, defaults=[\'2\', \'None\', \'valid\', \'channels_last\'], "
|
||||
}
|
||||
member_method {
|
||||
name: "add_loss"
|
||||
|
||||
@ -70,6 +70,6 @@ tf_module {
|
||||
}
|
||||
member_method {
|
||||
name: "to_categorical"
|
||||
argspec: "args=[\'y\', \'num_classes\'], varargs=None, keywords=None, defaults=[\'None\'], "
|
||||
argspec: "args=[\'y\', \'num_classes\', \'dtype\'], varargs=None, keywords=None, defaults=[\'None\', \'float32\'], "
|
||||
}
|
||||
}
|
||||
|
||||
@ -8,6 +8,10 @@ tf_module {
|
||||
name: "elu"
|
||||
argspec: "args=[\'x\', \'alpha\'], varargs=None, keywords=None, defaults=[\'1.0\'], "
|
||||
}
|
||||
member_method {
|
||||
name: "exponential"
|
||||
argspec: "args=[\'x\'], varargs=None, keywords=None, defaults=None"
|
||||
}
|
||||
member_method {
|
||||
name: "get"
|
||||
argspec: "args=[\'identifier\'], varargs=None, keywords=None, defaults=None"
|
||||
|
||||
@ -46,7 +46,7 @@ tf_module {
|
||||
}
|
||||
member_method {
|
||||
name: "batch_normalization"
|
||||
argspec: "args=[\'x\', \'mean\', \'var\', \'beta\', \'gamma\', \'epsilon\'], varargs=None, keywords=None, defaults=[\'0.001\'], "
|
||||
argspec: "args=[\'x\', \'mean\', \'var\', \'beta\', \'gamma\', \'axis\', \'epsilon\'], varargs=None, keywords=None, defaults=[\'-1\', \'0.001\'], "
|
||||
}
|
||||
member_method {
|
||||
name: "batch_set_value"
|
||||
@ -98,7 +98,7 @@ tf_module {
|
||||
}
|
||||
member_method {
|
||||
name: "conv2d_transpose"
|
||||
argspec: "args=[\'x\', \'kernel\', \'output_shape\', \'strides\', \'padding\', \'data_format\'], varargs=None, keywords=None, defaults=[\'(1, 1)\', \'valid\', \'None\'], "
|
||||
argspec: "args=[\'x\', \'kernel\', \'output_shape\', \'strides\', \'padding\', \'data_format\', \'dilation_rate\'], varargs=None, keywords=None, defaults=[\'(1, 1)\', \'valid\', \'None\', \'(1, 1)\'], "
|
||||
}
|
||||
member_method {
|
||||
name: "conv3d"
|
||||
|
||||
@ -83,7 +83,7 @@ tf_class {
|
||||
}
|
||||
member_method {
|
||||
name: "__init__"
|
||||
argspec: "args=[\'self\', \'pool_size\', \'strides\', \'padding\', \'data_format\'], varargs=None, keywords=kwargs, defaults=[\'2\', \'None\', \'valid\', \'None\'], "
|
||||
argspec: "args=[\'self\', \'pool_size\', \'strides\', \'padding\', \'data_format\'], varargs=None, keywords=kwargs, defaults=[\'2\', \'None\', \'valid\', \'channels_last\'], "
|
||||
}
|
||||
member_method {
|
||||
name: "add_loss"
|
||||
|
||||
@ -83,7 +83,7 @@ tf_class {
|
||||
}
|
||||
member_method {
|
||||
name: "__init__"
|
||||
argspec: "args=[\'self\', \'pool_size\', \'strides\', \'padding\', \'data_format\'], varargs=None, keywords=kwargs, defaults=[\'2\', \'None\', \'valid\', \'None\'], "
|
||||
argspec: "args=[\'self\', \'pool_size\', \'strides\', \'padding\', \'data_format\'], varargs=None, keywords=kwargs, defaults=[\'2\', \'None\', \'valid\', \'channels_last\'], "
|
||||
}
|
||||
member_method {
|
||||
name: "add_loss"
|
||||
|
||||
@ -84,7 +84,7 @@ tf_class {
|
||||
}
|
||||
member_method {
|
||||
name: "__init__"
|
||||
argspec: "args=[\'self\', \'filters\', \'kernel_size\', \'strides\', \'padding\', \'data_format\', \'activation\', \'use_bias\', \'kernel_initializer\', \'bias_initializer\', \'kernel_regularizer\', \'bias_regularizer\', \'activity_regularizer\', \'kernel_constraint\', \'bias_constraint\'], varargs=None, keywords=kwargs, defaults=[\'(1, 1)\', \'valid\', \'None\', \'None\', \'True\', \'glorot_uniform\', \'zeros\', \'None\', \'None\', \'None\', \'None\', \'None\'], "
|
||||
argspec: "args=[\'self\', \'filters\', \'kernel_size\', \'strides\', \'padding\', \'output_padding\', \'data_format\', \'dilation_rate\', \'activation\', \'use_bias\', \'kernel_initializer\', \'bias_initializer\', \'kernel_regularizer\', \'bias_regularizer\', \'activity_regularizer\', \'kernel_constraint\', \'bias_constraint\'], varargs=None, keywords=kwargs, defaults=[\'(1, 1)\', \'valid\', \'None\', \'None\', \'(1, 1)\', \'None\', \'True\', \'glorot_uniform\', \'zeros\', \'None\', \'None\', \'None\', \'None\', \'None\'], "
|
||||
}
|
||||
member_method {
|
||||
name: "add_loss"
|
||||
|
||||
@ -84,7 +84,7 @@ tf_class {
|
||||
}
|
||||
member_method {
|
||||
name: "__init__"
|
||||
argspec: "args=[\'self\', \'filters\', \'kernel_size\', \'strides\', \'padding\', \'data_format\', \'activation\', \'use_bias\', \'kernel_initializer\', \'bias_initializer\', \'kernel_regularizer\', \'bias_regularizer\', \'activity_regularizer\', \'kernel_constraint\', \'bias_constraint\'], varargs=None, keywords=kwargs, defaults=[\'(1, 1, 1)\', \'valid\', \'None\', \'None\', \'True\', \'glorot_uniform\', \'zeros\', \'None\', \'None\', \'None\', \'None\', \'None\'], "
|
||||
argspec: "args=[\'self\', \'filters\', \'kernel_size\', \'strides\', \'padding\', \'output_padding\', \'data_format\', \'activation\', \'use_bias\', \'kernel_initializer\', \'bias_initializer\', \'kernel_regularizer\', \'bias_regularizer\', \'activity_regularizer\', \'kernel_constraint\', \'bias_constraint\'], varargs=None, keywords=kwargs, defaults=[\'(1, 1, 1)\', \'valid\', \'None\', \'None\', \'None\', \'True\', \'glorot_uniform\', \'zeros\', \'None\', \'None\', \'None\', \'None\', \'None\'], "
|
||||
}
|
||||
member_method {
|
||||
name: "add_loss"
|
||||
|
||||
@ -84,7 +84,7 @@ tf_class {
|
||||
}
|
||||
member_method {
|
||||
name: "__init__"
|
||||
argspec: "args=[\'self\', \'filters\', \'kernel_size\', \'strides\', \'padding\', \'data_format\', \'activation\', \'use_bias\', \'kernel_initializer\', \'bias_initializer\', \'kernel_regularizer\', \'bias_regularizer\', \'activity_regularizer\', \'kernel_constraint\', \'bias_constraint\'], varargs=None, keywords=kwargs, defaults=[\'(1, 1)\', \'valid\', \'None\', \'None\', \'True\', \'glorot_uniform\', \'zeros\', \'None\', \'None\', \'None\', \'None\', \'None\'], "
|
||||
argspec: "args=[\'self\', \'filters\', \'kernel_size\', \'strides\', \'padding\', \'output_padding\', \'data_format\', \'dilation_rate\', \'activation\', \'use_bias\', \'kernel_initializer\', \'bias_initializer\', \'kernel_regularizer\', \'bias_regularizer\', \'activity_regularizer\', \'kernel_constraint\', \'bias_constraint\'], varargs=None, keywords=kwargs, defaults=[\'(1, 1)\', \'valid\', \'None\', \'None\', \'(1, 1)\', \'None\', \'True\', \'glorot_uniform\', \'zeros\', \'None\', \'None\', \'None\', \'None\', \'None\'], "
|
||||
}
|
||||
member_method {
|
||||
name: "add_loss"
|
||||
|
||||
@ -84,7 +84,7 @@ tf_class {
|
||||
}
|
||||
member_method {
|
||||
name: "__init__"
|
||||
argspec: "args=[\'self\', \'filters\', \'kernel_size\', \'strides\', \'padding\', \'data_format\', \'activation\', \'use_bias\', \'kernel_initializer\', \'bias_initializer\', \'kernel_regularizer\', \'bias_regularizer\', \'activity_regularizer\', \'kernel_constraint\', \'bias_constraint\'], varargs=None, keywords=kwargs, defaults=[\'(1, 1, 1)\', \'valid\', \'None\', \'None\', \'True\', \'glorot_uniform\', \'zeros\', \'None\', \'None\', \'None\', \'None\', \'None\'], "
|
||||
argspec: "args=[\'self\', \'filters\', \'kernel_size\', \'strides\', \'padding\', \'output_padding\', \'data_format\', \'activation\', \'use_bias\', \'kernel_initializer\', \'bias_initializer\', \'kernel_regularizer\', \'bias_regularizer\', \'activity_regularizer\', \'kernel_constraint\', \'bias_constraint\'], varargs=None, keywords=kwargs, defaults=[\'(1, 1, 1)\', \'valid\', \'None\', \'None\', \'None\', \'True\', \'glorot_uniform\', \'zeros\', \'None\', \'None\', \'None\', \'None\', \'None\'], "
|
||||
}
|
||||
member_method {
|
||||
name: "add_loss"
|
||||
|
||||
@ -83,7 +83,7 @@ tf_class {
|
||||
}
|
||||
member_method {
|
||||
name: "__init__"
|
||||
argspec: "args=[\'self\'], varargs=None, keywords=kwargs, defaults=None"
|
||||
argspec: "args=[\'self\', \'data_format\'], varargs=None, keywords=kwargs, defaults=[\'channels_last\'], "
|
||||
}
|
||||
member_method {
|
||||
name: "add_loss"
|
||||
@ -111,7 +111,7 @@ tf_class {
|
||||
}
|
||||
member_method {
|
||||
name: "call"
|
||||
argspec: "args=[\'self\', \'inputs\'], varargs=None, keywords=None, defaults=None"
|
||||
argspec: "args=[\'self\', \'inputs\', \'mask\'], varargs=None, keywords=None, defaults=[\'None\'], "
|
||||
}
|
||||
member_method {
|
||||
name: "compute_mask"
|
||||
|
||||
@ -83,7 +83,7 @@ tf_class {
|
||||
}
|
||||
member_method {
|
||||
name: "__init__"
|
||||
argspec: "args=[\'self\'], varargs=None, keywords=kwargs, defaults=None"
|
||||
argspec: "args=[\'self\', \'data_format\'], varargs=None, keywords=kwargs, defaults=[\'channels_last\'], "
|
||||
}
|
||||
member_method {
|
||||
name: "add_loss"
|
||||
@ -111,7 +111,7 @@ tf_class {
|
||||
}
|
||||
member_method {
|
||||
name: "call"
|
||||
argspec: "args=[\'self\', \'inputs\'], varargs=None, keywords=None, defaults=None"
|
||||
argspec: "args=[\'self\', \'inputs\', \'mask\'], varargs=None, keywords=None, defaults=[\'None\'], "
|
||||
}
|
||||
member_method {
|
||||
name: "compute_mask"
|
||||
|
||||
@ -83,7 +83,7 @@ tf_class {
|
||||
}
|
||||
member_method {
|
||||
name: "__init__"
|
||||
argspec: "args=[\'self\'], varargs=None, keywords=kwargs, defaults=None"
|
||||
argspec: "args=[\'self\', \'data_format\'], varargs=None, keywords=kwargs, defaults=[\'channels_last\'], "
|
||||
}
|
||||
member_method {
|
||||
name: "add_loss"
|
||||
|
||||
@ -83,7 +83,7 @@ tf_class {
|
||||
}
|
||||
member_method {
|
||||
name: "__init__"
|
||||
argspec: "args=[\'self\'], varargs=None, keywords=kwargs, defaults=None"
|
||||
argspec: "args=[\'self\', \'data_format\'], varargs=None, keywords=kwargs, defaults=[\'channels_last\'], "
|
||||
}
|
||||
member_method {
|
||||
name: "add_loss"
|
||||
|
||||
@ -83,7 +83,7 @@ tf_class {
|
||||
}
|
||||
member_method {
|
||||
name: "__init__"
|
||||
argspec: "args=[\'self\', \'pool_size\', \'strides\', \'padding\', \'data_format\'], varargs=None, keywords=kwargs, defaults=[\'2\', \'None\', \'valid\', \'None\'], "
|
||||
argspec: "args=[\'self\', \'pool_size\', \'strides\', \'padding\', \'data_format\'], varargs=None, keywords=kwargs, defaults=[\'2\', \'None\', \'valid\', \'channels_last\'], "
|
||||
}
|
||||
member_method {
|
||||
name: "add_loss"
|
||||
|
||||
@ -83,7 +83,7 @@ tf_class {
|
||||
}
|
||||
member_method {
|
||||
name: "__init__"
|
||||
argspec: "args=[\'self\', \'pool_size\', \'strides\', \'padding\', \'data_format\'], varargs=None, keywords=kwargs, defaults=[\'2\', \'None\', \'valid\', \'None\'], "
|
||||
argspec: "args=[\'self\', \'pool_size\', \'strides\', \'padding\', \'data_format\'], varargs=None, keywords=kwargs, defaults=[\'2\', \'None\', \'valid\', \'channels_last\'], "
|
||||
}
|
||||
member_method {
|
||||
name: "add_loss"
|
||||
|
||||
@ -70,6 +70,6 @@ tf_module {
|
||||
}
|
||||
member_method {
|
||||
name: "to_categorical"
|
||||
argspec: "args=[\'y\', \'num_classes\'], varargs=None, keywords=None, defaults=[\'None\'], "
|
||||
argspec: "args=[\'y\', \'num_classes\', \'dtype\'], varargs=None, keywords=None, defaults=[\'None\', \'float32\'], "
|
||||
}
|
||||
}
|
||||
|
||||
Loading…
Reference in New Issue
Block a user