1 # Copyright 2016 The TensorFlow Authors. All Rights Reserved. 2 # 3 # Licensed under the Apache License, Version 2.0 (the "License"); 4 # you may not use this file except in compliance with the License. 5 # You may obtain a copy of the License at 6 # 7 # http://www.apache.org/licenses/LICENSE-2.0 8 # 9 # Unless required by applicable law or agreed to in writing, software 10 # distributed under the License is distributed on an "AS IS" BASIS, 11 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 12 # See the License for the specific language governing permissions and 13 # limitations under the License. 14 # ============================================================================== 15 """Tests for Keras backend.""" 16 from __future__ import absolute_import 17 from __future__ import division 18 from __future__ import print_function 19 20 from absl.testing import parameterized 21 import numpy as np 22 import scipy.sparse 23 24 from tensorflow.core.protobuf import config_pb2 25 from tensorflow.python import keras 26 from tensorflow.python.eager import context 27 from tensorflow.python.framework import dtypes 28 from tensorflow.python.framework import errors_impl 29 from tensorflow.python.framework import ops 30 from tensorflow.python.framework import sparse_tensor 31 from tensorflow.python.framework import test_util 32 from tensorflow.python.ops import array_ops 33 from tensorflow.python.ops import nn 34 from tensorflow.python.ops import variables 35 from tensorflow.python.platform import test 36 from tensorflow.python.util import tf_inspect 37 38 39 def compare_single_input_op_to_numpy(keras_op, 40 np_op, 41 input_shape, 42 dtype='float32', 43 negative_values=True, 44 keras_args=None, 45 keras_kwargs=None, 46 np_args=None, 47 np_kwargs=None): 48 keras_args = keras_args or [] 49 keras_kwargs = keras_kwargs or {} 50 np_args = np_args or [] 51 np_kwargs = np_kwargs or {} 52 inputs = 2. * np.random.random(input_shape) 53 if negative_values: 54 inputs -= 1. 55 keras_output = keras_op(keras.backend.variable(inputs, dtype=dtype), 56 *keras_args, **keras_kwargs) 57 keras_output = keras.backend.eval(keras_output) 58 np_output = np_op(inputs.astype(dtype), *np_args, **np_kwargs) 59 try: 60 np.testing.assert_allclose(keras_output, np_output, atol=1e-4) 61 except AssertionError: 62 raise AssertionError('Test for op `' + str(keras_op.__name__) + '` failed; ' 63 'Expected ' + str(np_output) + ' but got ' + 64 str(keras_output)) 65 66 67 def compare_two_inputs_op_to_numpy(keras_op, 68 np_op, 69 input_shape_a, 70 input_shape_b, 71 dtype='float32', 72 keras_args=None, 73 keras_kwargs=None, 74 np_args=None, 75 np_kwargs=None): 76 keras_args = keras_args or [] 77 keras_kwargs = keras_kwargs or {} 78 np_args = np_args or [] 79 np_kwargs = np_kwargs or {} 80 input_a = np.random.random(input_shape_a) 81 input_b = np.random.random(input_shape_b) 82 keras_output = keras_op(keras.backend.variable(input_a, dtype=dtype), 83 keras.backend.variable(input_b, dtype=dtype), 84 *keras_args, **keras_kwargs) 85 keras_output = keras.backend.eval(keras_output) 86 np_output = np_op(input_a.astype(dtype), input_b.astype(dtype), 87 *np_args, **np_kwargs) 88 try: 89 np.testing.assert_allclose(keras_output, np_output, atol=1e-4) 90 except AssertionError: 91 raise AssertionError('Test for op `' + str(keras_op.__name__) + '` failed; ' 92 'Expected ' + str(np_output) + ' but got ' + 93 str(keras_output)) 94 95 96 @test_util.run_all_in_graph_and_eager_modes 97 class BackendUtilsTest(test.TestCase): 98 99 def test_backend(self): 100 self.assertEqual(keras.backend.backend(), 'tensorflow') 101 102 def test_get_reset_uids(self): 103 self.assertEqual(keras.backend.get_uid('foo'), 1) 104 self.assertEqual(keras.backend.get_uid('foo'), 2) 105 106 keras.backend.reset_uids() 107 self.assertEqual(keras.backend.get_uid('foo'), 1) 108 109 def test_learning_phase(self): 110 with self.cached_session() as sess: 111 with self.assertRaises(ValueError): 112 keras.backend.set_learning_phase(2) 113 114 # Test running with a learning-phase-consuming layer 115 with keras.backend.learning_phase_scope(0): 116 x = keras.Input((3,)) 117 y = keras.layers.BatchNormalization()(x) 118 if not context.executing_eagerly(): 119 self.evaluate(variables.global_variables_initializer()) 120 sess.run(y, feed_dict={x: np.random.random((2, 3))}) 121 122 def test_learning_phase_name(self): 123 with ops.name_scope('test_scope'): 124 # Test that outer name scopes do not affect the learning phase's name. 125 lp = keras.backend.symbolic_learning_phase() 126 self.assertEqual(lp.name, 'keras_learning_phase:0') 127 128 def test_learning_phase_scope(self): 129 initial_learning_phase = keras.backend.learning_phase() 130 with keras.backend.learning_phase_scope(1): 131 self.assertEqual(keras.backend.learning_phase(), 1) 132 self.assertEqual(keras.backend.learning_phase(), initial_learning_phase) 133 with keras.backend.learning_phase_scope(0): 134 self.assertEqual(keras.backend.learning_phase(), 0) 135 self.assertEqual(keras.backend.learning_phase(), initial_learning_phase) 136 with self.assertRaises(ValueError): 137 with keras.backend.learning_phase_scope(None): 138 pass 139 self.assertEqual(keras.backend.learning_phase(), initial_learning_phase) 140 141 new_learning_phase = 0 142 keras.backend.set_learning_phase(new_learning_phase) 143 self.assertEqual(keras.backend.learning_phase(), new_learning_phase) 144 with keras.backend.learning_phase_scope(1): 145 self.assertEqual(keras.backend.learning_phase(), 1) 146 self.assertEqual(keras.backend.learning_phase(), new_learning_phase) 147 148 def test_learning_phase_scope_in_graph(self): 149 initial_learning_phase_outside_graph = keras.backend.learning_phase() 150 with keras.backend.get_graph().as_default(): 151 initial_learning_phase_in_graph = keras.backend.learning_phase() 152 153 self.assertEqual(keras.backend.learning_phase(), 154 initial_learning_phase_outside_graph) 155 with keras.backend.learning_phase_scope(1): 156 self.assertEqual(keras.backend.learning_phase(), 1) 157 self.assertEqual(keras.backend.learning_phase(), 158 initial_learning_phase_outside_graph) 159 160 with keras.backend.get_graph().as_default(): 161 self.assertEqual(keras.backend.learning_phase(), 162 initial_learning_phase_in_graph) 163 164 self.assertEqual(keras.backend.learning_phase(), 165 initial_learning_phase_outside_graph) 166 167 def test_int_shape(self): 168 x = keras.backend.ones(shape=(3, 4)) 169 self.assertEqual(keras.backend.int_shape(x), (3, 4)) 170 171 if not context.executing_eagerly(): 172 x = keras.backend.placeholder(shape=(None, 4)) 173 self.assertEqual(keras.backend.int_shape(x), (None, 4)) 174 175 def test_in_train_phase(self): 176 y1 = keras.backend.variable(1) 177 y2 = keras.backend.variable(2) 178 if context.executing_eagerly(): 179 with keras.backend.learning_phase_scope(0): 180 y_val_test = keras.backend.in_train_phase(y1, y2).numpy() 181 with keras.backend.learning_phase_scope(1): 182 y_val_train = keras.backend.in_train_phase(y1, y2).numpy() 183 else: 184 y = keras.backend.in_train_phase(y1, y2) 185 f = keras.backend.function([keras.backend.learning_phase()], [y]) 186 y_val_test = f([0])[0] 187 y_val_train = f([1])[0] 188 self.assertAllClose(y_val_test, 2) 189 self.assertAllClose(y_val_train, 1) 190 191 def test_is_keras_tensor(self): 192 x = keras.backend.variable(1) 193 self.assertEqual(keras.backend.is_keras_tensor(x), False) 194 x = keras.Input(shape=(1,)) 195 self.assertEqual(keras.backend.is_keras_tensor(x), True) 196 with self.assertRaises(ValueError): 197 keras.backend.is_keras_tensor(0) 198 199 def test_stop_gradient(self): 200 x = keras.backend.variable(1) 201 y = keras.backend.stop_gradient(x) 202 if not context.executing_eagerly(): 203 self.assertEqual(y.op.name[:12], 'StopGradient') 204 205 xs = [keras.backend.variable(1) for _ in range(3)] 206 ys = keras.backend.stop_gradient(xs) 207 if not context.executing_eagerly(): 208 for y in ys: 209 self.assertEqual(y.op.name[:12], 'StopGradient') 210 211 212 @test_util.run_all_in_graph_and_eager_modes 213 class BackendVariableTest(test.TestCase): 214 215 def test_zeros(self): 216 x = keras.backend.zeros((3, 4)) 217 val = keras.backend.eval(x) 218 self.assertAllClose(val, np.zeros((3, 4))) 219 220 def test_ones(self): 221 x = keras.backend.ones((3, 4)) 222 val = keras.backend.eval(x) 223 self.assertAllClose(val, np.ones((3, 4))) 224 225 def test_eye(self): 226 x = keras.backend.eye(4) 227 val = keras.backend.eval(x) 228 self.assertAllClose(val, np.eye(4)) 229 230 def test_zeros_like(self): 231 x = keras.backend.zeros((3, 4)) 232 y = keras.backend.zeros_like(x) 233 val = keras.backend.eval(y) 234 self.assertAllClose(val, np.zeros((3, 4))) 235 236 def test_ones_like(self): 237 x = keras.backend.zeros((3, 4)) 238 y = keras.backend.ones_like(x) 239 val = keras.backend.eval(y) 240 self.assertAllClose(val, np.ones((3, 4))) 241 242 def test_random_uniform_variable(self): 243 x = keras.backend.random_uniform_variable((30, 20), low=1, high=2, seed=0) 244 val = keras.backend.eval(x) 245 self.assertAllClose(val.mean(), 1.5, atol=1e-1) 246 self.assertAllClose(val.max(), 2., atol=1e-1) 247 self.assertAllClose(val.min(), 1., atol=1e-1) 248 249 def test_random_normal_variable(self): 250 x = keras.backend.random_normal_variable((30, 20), 1., 0.5, seed=0) 251 val = keras.backend.eval(x) 252 self.assertAllClose(val.mean(), 1., atol=1e-1) 253 self.assertAllClose(val.std(), 0.5, atol=1e-1) 254 255 def test_count_params(self): 256 x = keras.backend.zeros((4, 5)) 257 val = keras.backend.count_params(x) 258 self.assertAllClose(val, 20) 259 260 def test_constant(self): 261 ref_val = np.random.random((3, 4)).astype('float32') 262 x = keras.backend.constant(ref_val) 263 val = keras.backend.eval(x) 264 self.assertAllClose(val, ref_val) 265 266 def test_sparse_variable(self): 267 val = scipy.sparse.eye(10) 268 x = keras.backend.variable(val) 269 self.assertTrue(isinstance(x, sparse_tensor.SparseTensor)) 270 271 y = keras.backend.to_dense(x) 272 self.assertFalse(keras.backend.is_sparse(y)) 273 274 275 @test_util.run_all_in_graph_and_eager_modes 276 class BackendLinearAlgebraTest(test.TestCase): 277 278 def test_dot(self): 279 x = keras.backend.ones(shape=(2, 3)) 280 y = keras.backend.ones(shape=(3, 4)) 281 xy = keras.backend.dot(x, y) 282 self.assertEqual(xy.shape.as_list(), [2, 4]) 283 284 x = keras.backend.ones(shape=(32, 28, 3)) 285 y = keras.backend.ones(shape=(3, 4)) 286 xy = keras.backend.dot(x, y) 287 self.assertEqual(xy.shape.as_list(), [32, 28, 4]) 288 289 def test_batch_dot(self): 290 x = keras.backend.ones(shape=(32, 20, 1)) 291 y = keras.backend.ones(shape=(32, 30, 20)) 292 xy = keras.backend.batch_dot(x, y, axes=[1, 2]) 293 self.assertEqual(xy.shape.as_list(), [32, 1, 30]) 294 295 # TODO(fchollet): insufficiently tested. 296 297 def test_reduction_ops(self): 298 ops_to_test = [ 299 (keras.backend.max, np.max), 300 (keras.backend.min, np.min), 301 (keras.backend.sum, np.sum), 302 (keras.backend.prod, np.prod), 303 (keras.backend.var, np.var), 304 (keras.backend.std, np.std), 305 (keras.backend.mean, np.mean), 306 (keras.backend.argmin, np.argmin), 307 (keras.backend.argmax, np.argmax), 308 ] 309 for keras_op, np_op in ops_to_test: 310 compare_single_input_op_to_numpy(keras_op, np_op, input_shape=(4, 7, 5), 311 keras_kwargs={'axis': 1}, 312 np_kwargs={'axis': 1}) 313 compare_single_input_op_to_numpy(keras_op, np_op, input_shape=(4, 7, 5), 314 keras_kwargs={'axis': -1}, 315 np_kwargs={'axis': -1}) 316 if 'keepdims' in tf_inspect.getargspec(keras_op).args: 317 compare_single_input_op_to_numpy(keras_op, np_op, 318 input_shape=(4, 7, 5), 319 keras_kwargs={'axis': 1, 320 'keepdims': True}, 321 np_kwargs={'axis': 1, 322 'keepdims': True}) 323 324 def test_elementwise_ops(self): 325 ops_to_test = [ 326 (keras.backend.square, np.square), 327 (keras.backend.abs, np.abs), 328 (keras.backend.round, np.round), 329 (keras.backend.sign, np.sign), 330 (keras.backend.sin, np.sin), 331 (keras.backend.cos, np.cos), 332 (keras.backend.exp, np.exp), 333 ] 334 for keras_op, np_op in ops_to_test: 335 compare_single_input_op_to_numpy(keras_op, np_op, input_shape=(4, 7)) 336 337 ops_to_test = [ 338 (keras.backend.sqrt, np.sqrt), 339 (keras.backend.log, np.log), 340 ] 341 for keras_op, np_op in ops_to_test: 342 compare_single_input_op_to_numpy(keras_op, np_op, 343 input_shape=(4, 7), 344 negative_values=False) 345 346 compare_single_input_op_to_numpy( 347 keras.backend.clip, np.clip, 348 input_shape=(6, 4), 349 keras_kwargs={'min_value': 0.1, 'max_value': 2.4}, 350 np_kwargs={'a_min': 0.1, 'a_max': 1.4}) 351 352 compare_single_input_op_to_numpy( 353 keras.backend.pow, np.power, 354 input_shape=(6, 4), 355 keras_args=[3], 356 np_args=[3]) 357 358 def test_two_tensor_ops(self): 359 ops_to_test = [ 360 (keras.backend.equal, np.equal), 361 (keras.backend.not_equal, np.not_equal), 362 (keras.backend.greater, np.greater), 363 (keras.backend.greater_equal, np.greater_equal), 364 (keras.backend.less, np.less), 365 (keras.backend.less_equal, np.less_equal), 366 (keras.backend.maximum, np.maximum), 367 (keras.backend.minimum, np.minimum), 368 ] 369 for keras_op, np_op in ops_to_test: 370 compare_two_inputs_op_to_numpy(keras_op, np_op, 371 input_shape_a=(4, 7), 372 input_shape_b=(4, 7)) 373 374 def test_relu(self): 375 x = ops.convert_to_tensor([[-4, 0], [2, 7]], 'float32') 376 377 # standard relu 378 relu_op = keras.backend.relu(x) 379 self.assertAllClose(keras.backend.eval(relu_op), [[0, 0], [2, 7]]) 380 381 # alpha (leaky relu used) 382 relu_op = keras.backend.relu(x, alpha=0.5) 383 if not context.executing_eagerly(): 384 self.assertTrue('LeakyRelu' in relu_op.name) 385 self.assertAllClose(keras.backend.eval(relu_op), [[-2, 0], [2, 7]]) 386 387 # max_value < some elements 388 relu_op = keras.backend.relu(x, max_value=5) 389 self.assertAllClose(keras.backend.eval(relu_op), [[0, 0], [2, 5]]) 390 391 # nn.relu6 used 392 relu_op = keras.backend.relu(x, max_value=6) 393 if not context.executing_eagerly(): 394 self.assertTrue('Relu6' in relu_op.name) # uses tf.nn.relu6 395 self.assertAllClose(keras.backend.eval(relu_op), [[0, 0], [2, 6]]) 396 397 # max value > 6 398 relu_op = keras.backend.relu(x, max_value=10) 399 self.assertAllClose(keras.backend.eval(relu_op), [[0, 0], [2, 7]]) 400 401 # max value is float 402 relu_op = keras.backend.relu(x, max_value=4.3) 403 self.assertAllClose(keras.backend.eval(relu_op), [[0, 0], [2, 4.3]]) 404 405 # max value == 0 406 relu_op = keras.backend.relu(x, max_value=0) 407 self.assertAllClose(keras.backend.eval(relu_op), [[0, 0], [0, 0]]) 408 409 # alpha and max_value 410 relu_op = keras.backend.relu(x, alpha=0.25, max_value=3) 411 self.assertAllClose(keras.backend.eval(relu_op), [[-1, 0], [2, 3]]) 412 413 # threshold 414 relu_op = keras.backend.relu(x, threshold=3) 415 self.assertAllClose(keras.backend.eval(relu_op), [[0, 0], [0, 7]]) 416 417 # threshold is float 418 relu_op = keras.backend.relu(x, threshold=1.5) 419 self.assertAllClose(keras.backend.eval(relu_op), [[0, 0], [2, 7]]) 420 421 # threshold is negative 422 relu_op = keras.backend.relu(x, threshold=-5) 423 self.assertAllClose(keras.backend.eval(relu_op), [[-4, 0], [2, 7]]) 424 425 # threshold and max_value 426 relu_op = keras.backend.relu(x, threshold=3, max_value=5) 427 self.assertAllClose(keras.backend.eval(relu_op), [[0, 0], [0, 5]]) 428 429 # threshold and alpha 430 relu_op = keras.backend.relu(x, alpha=0.25, threshold=4) 431 self.assertAllClose(keras.backend.eval(relu_op), [[-2, -1], [-0.5, 7]]) 432 433 # threshold, alpha, and max_value 434 relu_op = keras.backend.relu(x, alpha=0.25, threshold=4, max_value=5) 435 self.assertAllClose(keras.backend.eval(relu_op), [[-2, -1], [-0.5, 5]]) 436 437 438 @test_util.run_all_in_graph_and_eager_modes 439 class BackendShapeOpsTest(test.TestCase): 440 441 def test_reshape(self): 442 compare_single_input_op_to_numpy(keras.backend.reshape, np.reshape, 443 input_shape=(4, 7), 444 keras_args=[(2, 14)], 445 np_args=[(2, 14)]) 446 447 def test_concatenate(self): 448 a = keras.backend.variable(np.ones((1, 2, 3))) 449 b = keras.backend.variable(np.ones((1, 2, 2))) 450 y = keras.backend.concatenate([a, b], axis=-1) 451 self.assertEqual(y.shape.as_list(), [1, 2, 5]) 452 453 def test_permute_dimensions(self): 454 compare_single_input_op_to_numpy(keras.backend.permute_dimensions, 455 np.transpose, 456 input_shape=(4, 7), 457 keras_args=[(1, 0)], 458 np_args=[(1, 0)]) 459 460 def test_resize_images(self): 461 height_factor = 2 462 width_factor = 2 463 data_format = 'channels_last' 464 x = keras.backend.variable(np.ones((1, 2, 2, 3))) 465 y = keras.backend.resize_images(x, 466 height_factor, 467 width_factor, 468 data_format) 469 self.assertEqual(y.shape.as_list(), [1, 4, 4, 3]) 470 471 data_format = 'channels_first' 472 x = keras.backend.variable(np.ones((1, 3, 2, 2))) 473 y = keras.backend.resize_images(x, 474 height_factor, 475 width_factor, 476 data_format) 477 self.assertEqual(y.shape.as_list(), [1, 3, 4, 4]) 478 479 # Invalid use: 480 with self.assertRaises(ValueError): 481 keras.backend.resize_images(x, 482 height_factor, 483 width_factor, 484 data_format='unknown') 485 486 def test_resize_volumes(self): 487 height_factor = 2 488 width_factor = 2 489 depth_factor = 2 490 data_format = 'channels_last' 491 x = keras.backend.variable(np.ones((1, 2, 2, 2, 3))) 492 y = keras.backend.resize_volumes(x, 493 depth_factor, 494 height_factor, 495 width_factor, 496 data_format) 497 self.assertEqual(y.shape.as_list(), [1, 4, 4, 4, 3]) 498 499 data_format = 'channels_first' 500 x = keras.backend.variable(np.ones((1, 3, 2, 2, 2))) 501 y = keras.backend.resize_volumes(x, 502 depth_factor, 503 height_factor, 504 width_factor, 505 data_format) 506 self.assertEqual(y.shape.as_list(), [1, 3, 4, 4, 4]) 507 508 # Invalid use: 509 with self.assertRaises(ValueError): 510 keras.backend.resize_volumes(x, 511 depth_factor, 512 height_factor, 513 width_factor, 514 data_format='unknown') 515 516 def test_repeat_elements(self): 517 x = keras.backend.variable(np.ones((1, 3, 2))) 518 y = keras.backend.repeat_elements(x, 3, axis=1) 519 self.assertEqual(y.shape.as_list(), [1, 9, 2]) 520 521 # Use with a dynamic axis: 522 if not context.executing_eagerly(): 523 x = keras.backend.placeholder(shape=(2, None, 2)) 524 y = keras.backend.repeat_elements(x, 3, axis=1) 525 self.assertEqual(y.shape.as_list(), [2, None, 2]) 526 527 def test_repeat(self): 528 x = keras.backend.variable(np.ones((1, 3))) 529 y = keras.backend.repeat(x, 2) 530 self.assertEqual(y.shape.as_list(), [1, 2, 3]) 531 532 def test_flatten(self): 533 compare_single_input_op_to_numpy(keras.backend.flatten, 534 np.reshape, 535 input_shape=(4, 7, 6), 536 np_args=[(4 * 7 * 6,)]) 537 538 def test_batch_flatten(self): 539 compare_single_input_op_to_numpy(keras.backend.batch_flatten, 540 np.reshape, 541 input_shape=(4, 7, 6), 542 np_args=[(4, 7 * 6)]) 543 544 def test_temporal_padding(self): 545 546 def ref_op(x, padding): 547 shape = list(x.shape) 548 shape[1] += padding[0] + padding[1] 549 y = np.zeros(tuple(shape)) 550 y[:, padding[0]:-padding[1], :] = x 551 return y 552 553 compare_single_input_op_to_numpy(keras.backend.temporal_padding, 554 ref_op, 555 input_shape=(4, 7, 6), 556 keras_args=[(2, 3)], 557 np_args=[(2, 3)]) 558 559 def test_spatial_2d_padding(self): 560 561 def ref_op(x, padding, data_format='channels_last'): 562 shape = list(x.shape) 563 if data_format == 'channels_last': 564 shape[1] += padding[0][0] + padding[0][1] 565 shape[2] += padding[1][0] + padding[1][1] 566 y = np.zeros(tuple(shape)) 567 y[:, padding[0][0]:-padding[0][1], padding[1][0]:-padding[1][1], :] = x 568 else: 569 shape[2] += padding[0][0] + padding[0][1] 570 shape[3] += padding[1][0] + padding[1][1] 571 y = np.zeros(tuple(shape)) 572 y[:, :, padding[0][0]:-padding[0][1], padding[1][0]:-padding[1][1]] = x 573 return y 574 575 compare_single_input_op_to_numpy( 576 keras.backend.spatial_2d_padding, 577 ref_op, 578 input_shape=(2, 3, 2, 3), 579 keras_args=[((2, 3), (1, 2))], 580 keras_kwargs={'data_format': 'channels_last'}, 581 np_args=[((2, 3), (1, 2))], 582 np_kwargs={'data_format': 'channels_last'}) 583 compare_single_input_op_to_numpy( 584 keras.backend.spatial_2d_padding, 585 ref_op, 586 input_shape=(2, 3, 2, 3), 587 keras_args=[((2, 3), (1, 2))], 588 keras_kwargs={'data_format': 'channels_first'}, 589 np_args=[((2, 3), (1, 2))], 590 np_kwargs={'data_format': 'channels_first'}) 591 592 def test_spatial_3d_padding(self): 593 594 def ref_op(x, padding, data_format='channels_last'): 595 shape = list(x.shape) 596 if data_format == 'channels_last': 597 shape[1] += padding[0][0] + padding[0][1] 598 shape[2] += padding[1][0] + padding[1][1] 599 shape[3] += padding[2][0] + padding[2][1] 600 y = np.zeros(tuple(shape)) 601 y[:, 602 padding[0][0]:-padding[0][1], 603 padding[1][0]:-padding[1][1], 604 padding[2][0]:-padding[2][1], 605 :] = x 606 else: 607 shape[2] += padding[0][0] + padding[0][1] 608 shape[3] += padding[1][0] + padding[1][1] 609 shape[4] += padding[2][0] + padding[2][1] 610 y = np.zeros(tuple(shape)) 611 y[:, :, 612 padding[0][0]:-padding[0][1], 613 padding[1][0]:-padding[1][1], 614 padding[2][0]:-padding[2][1]] = x 615 return y 616 617 compare_single_input_op_to_numpy( 618 keras.backend.spatial_3d_padding, 619 ref_op, 620 input_shape=(2, 3, 2, 3, 2), 621 keras_args=[((2, 3), (1, 2), (2, 3))], 622 keras_kwargs={'data_format': 'channels_last'}, 623 np_args=[((2, 3), (1, 2), (2, 3))], 624 np_kwargs={'data_format': 'channels_last'}) 625 compare_single_input_op_to_numpy( 626 keras.backend.spatial_3d_padding, 627 ref_op, 628 input_shape=(2, 3, 2, 3, 2), 629 keras_args=[((2, 3), (1, 2), (2, 3))], 630 keras_kwargs={'data_format': 'channels_first'}, 631 np_args=[((2, 3), (1, 2), (2, 3))], 632 np_kwargs={'data_format': 'channels_first'}) 633 634 635 @test_util.run_all_in_graph_and_eager_modes 636 class BackendNNOpsTest(test.TestCase, parameterized.TestCase): 637 638 def test_bias_add(self): 639 keras_op = keras.backend.bias_add 640 np_op = np.add 641 compare_two_inputs_op_to_numpy(keras_op, np_op, 642 input_shape_a=(4, 7), 643 input_shape_b=(7,)) 644 compare_two_inputs_op_to_numpy(keras_op, np_op, 645 input_shape_a=(4, 3, 7), 646 input_shape_b=(7,)) 647 compare_two_inputs_op_to_numpy(keras_op, np_op, 648 input_shape_a=(4, 3, 5, 7), 649 input_shape_b=(7,)) 650 compare_two_inputs_op_to_numpy(keras_op, np_op, 651 input_shape_a=(4, 3, 5, 2, 7), 652 input_shape_b=(7,)) 653 654 with self.assertRaises((ValueError, errors_impl.InvalidArgumentError)): 655 x = keras.backend.variable((3, 4)) 656 b = keras.backend.variable((3, 4)) 657 keras.backend.bias_add(x, b) 658 with self.assertRaises(ValueError): 659 x = keras.backend.variable((3, 4)) 660 b = keras.backend.variable((4,)) 661 keras.backend.bias_add(x, b, data_format='unknown') 662 663 def test_bias_add_channels_first(self): 664 665 def keras_op(x, b): 666 return keras.backend.bias_add(x, b, data_format='channels_first') 667 668 def np_op(x, b): 669 if x.ndim == 3: 670 b = b.reshape((1, b.shape[0], 1)) 671 if x.ndim == 4: 672 b = b.reshape((1, b.shape[0], 1, 1)) 673 return x + b 674 675 compare_two_inputs_op_to_numpy(keras_op, np_op, 676 input_shape_a=(4, 3, 7), 677 input_shape_b=(3,)) 678 compare_two_inputs_op_to_numpy(keras_op, np_op, 679 input_shape_a=(4, 3, 5, 7), 680 input_shape_b=(3,)) 681 682 def test_pool2d(self): 683 val = np.random.random((10, 3, 10, 10)) 684 x = keras.backend.variable(val) 685 y = keras.backend.pool2d(x, (2, 2), strides=(1, 1), 686 padding='valid', data_format='channels_first', 687 pool_mode='max') 688 self.assertEqual(y.shape.as_list(), [10, 3, 9, 9]) 689 690 y = keras.backend.pool2d(x, (2, 2), strides=(1, 1), 691 padding='valid', data_format='channels_first', 692 pool_mode='avg') 693 self.assertEqual(y.shape.as_list(), [10, 3, 9, 9]) 694 695 val = np.random.random((10, 10, 10, 3)) 696 x = keras.backend.variable(val) 697 y = keras.backend.pool2d(x, (2, 2), strides=(1, 1), 698 padding='valid', data_format='channels_last') 699 self.assertEqual(y.shape.as_list(), [10, 9, 9, 3]) 700 701 val = np.random.random((10, 10, 10, 3)) 702 x = keras.backend.variable(val) 703 y = keras.backend.pool2d(x, (2, 2), strides=(1, 1), 704 padding='same', data_format='channels_last') 705 self.assertEqual(y.shape.as_list(), [10, 10, 10, 3]) 706 707 val = np.random.random((10, 10, 10, 3)) 708 x = keras.backend.variable(val) 709 y = keras.backend.pool2d(x, (2, 2), strides=(2, 2), 710 padding='same', data_format='channels_last') 711 self.assertEqual(y.shape.as_list(), [10, 5, 5, 3]) 712 713 with self.assertRaises(ValueError): 714 y = keras.backend.pool2d(x, (2, 2), strides=(2, 2), 715 padding='other', data_format='channels_last') 716 with self.assertRaises(ValueError): 717 y = keras.backend.pool2d(x, (2, 2), strides=(2, 2), 718 data_format='other') 719 with self.assertRaises(ValueError): 720 y = keras.backend.pool2d(x, (2, 2, 2), strides=(2, 2)) 721 with self.assertRaises(ValueError): 722 y = keras.backend.pool2d(x, (2, 2), strides=(2, 2, 2)) 723 with self.assertRaises(ValueError): 724 y = keras.backend.pool2d(x, (2, 2), strides=(2, 2), pool_mode='other') 725 726 def test_pool3d(self): 727 val = np.random.random((10, 3, 10, 10, 10)) 728 x = keras.backend.variable(val) 729 y = keras.backend.pool3d(x, (2, 2, 2), strides=(1, 1, 1), 730 padding='valid', data_format='channels_first', 731 pool_mode='max') 732 self.assertEqual(y.shape.as_list(), [10, 3, 9, 9, 9]) 733 734 y = keras.backend.pool3d(x, (2, 2, 2), strides=(1, 1, 1), 735 padding='valid', data_format='channels_first', 736 pool_mode='avg') 737 self.assertEqual(y.shape.as_list(), [10, 3, 9, 9, 9]) 738 739 val = np.random.random((10, 10, 10, 10, 3)) 740 x = keras.backend.variable(val) 741 y = keras.backend.pool3d(x, (2, 2, 2), strides=(1, 1, 1), 742 padding='valid', data_format='channels_last') 743 self.assertEqual(y.shape.as_list(), [10, 9, 9, 9, 3]) 744 745 val = np.random.random((10, 10, 10, 10, 3)) 746 x = keras.backend.variable(val) 747 y = keras.backend.pool3d(x, (2, 2, 2), strides=(1, 1, 1), 748 padding='same', data_format='channels_last') 749 self.assertEqual(y.shape.as_list(), [10, 10, 10, 10, 3]) 750 751 val = np.random.random((10, 10, 10, 10, 3)) 752 x = keras.backend.variable(val) 753 y = keras.backend.pool3d(x, (2, 2, 2), strides=(2, 2, 2), 754 padding='same', data_format='channels_last') 755 self.assertEqual(y.shape.as_list(), [10, 5, 5, 5, 3]) 756 757 def test_conv1d(self): 758 val = np.random.random((10, 4, 10)) 759 x = keras.backend.variable(val) 760 kernel_val = np.random.random((3, 4, 5)) 761 k = keras.backend.variable(kernel_val) 762 y = keras.backend.conv1d(x, k, strides=(1,), 763 padding='valid', data_format='channels_first') 764 self.assertEqual(y.shape.as_list(), [10, 5, 8]) 765 766 val = np.random.random((10, 10, 4)) 767 x = keras.backend.variable(val) 768 y = keras.backend.conv1d(x, k, strides=(1,), 769 padding='valid', data_format='channels_last') 770 self.assertEqual(y.shape.as_list(), [10, 8, 5]) 771 772 val = np.random.random((10, 10, 4)) 773 x = keras.backend.variable(val) 774 y = keras.backend.conv1d(x, k, strides=(1,), 775 padding='same', data_format='channels_last') 776 self.assertEqual(y.shape.as_list(), [10, 10, 5]) 777 778 val = np.random.random((10, 10, 4)) 779 x = keras.backend.variable(val) 780 y = keras.backend.conv1d(x, k, strides=(2,), 781 padding='same', data_format='channels_last') 782 self.assertEqual(y.shape.as_list(), [10, 5, 5]) 783 784 def test_local_conv_channels_dim(self): 785 filters = 3 786 batch_size = 2 787 788 for input_shape in [(3, 5), (2, 3, 5), (2, 5, 3, 4)]: 789 channels_in = input_shape[0] 790 input_spatial_shape = input_shape[1:] 791 dim = len(input_spatial_shape) 792 793 inputs = np.random.normal(0, 1, (batch_size,) + input_shape) 794 inputs_cf = keras.backend.variable(inputs) 795 796 for kernel_size in [1, 2]: 797 for stride in [1, 2]: 798 kernel_sizes = (kernel_size,) * dim 799 strides = (stride,) * dim 800 801 output_shape = tuple([(i - kernel_size + stride) // stride 802 for i in input_spatial_shape]) 803 804 kernel_shape = (np.prod(output_shape), 805 np.prod(kernel_sizes) * channels_in, 806 filters) 807 808 kernel = np.random.normal( 809 0, 810 1, 811 output_shape + (channels_in, np.prod(kernel_sizes), filters) 812 ) 813 814 kernel_cf = np.reshape(kernel, kernel_shape) 815 kernel_cf = keras.backend.variable(kernel_cf) 816 817 conv_cf = keras.backend.local_conv(inputs_cf, 818 kernel_cf, 819 kernel_sizes, 820 strides, 821 output_shape, 822 'channels_first') 823 824 inputs_cl = np.transpose(inputs, [0, 2] + list(range(3, dim + 2)) + 825 [1]) 826 inputs_cl = keras.backend.variable(inputs_cl) 827 828 kernel_cl = np.reshape( 829 np.transpose(kernel, list(range(dim)) + [dim + 1, dim, dim + 2]), 830 kernel_shape 831 ) 832 kernel_cl = keras.backend.variable(kernel_cl) 833 834 conv_cl = keras.backend.local_conv(inputs_cl, 835 kernel_cl, 836 kernel_sizes, 837 strides, 838 output_shape, 839 'channels_last') 840 841 conv_cf = keras.backend.eval(conv_cf) 842 conv_cl = keras.backend.eval(conv_cl) 843 844 self.assertAllCloseAccordingToType( 845 conv_cf, 846 np.transpose(conv_cl, 847 [0, dim + 1] + list(range(1, dim + 1))), 848 atol=1e-5 849 ) 850 851 @parameterized.named_parameters( 852 ('local_conv1d', (5, 6), (3,), (1,), (3,)), 853 ('local_conv2d', (4, 5, 6), (3, 3), (1, 1), (2, 3))) 854 def test_local_conv_1d_and_2d(self, 855 input_shape, 856 kernel_sizes, 857 strides, 858 output_shape): 859 filters = 3 860 batch_size = 2 861 862 inputs = np.random.normal(0, 1, (batch_size,) + input_shape) 863 inputs = keras.backend.variable(inputs) 864 865 kernel = np.random.normal(0, 1, (np.prod(output_shape), 866 np.prod(kernel_sizes) * input_shape[-1], 867 filters)) 868 kernel = keras.backend.variable(kernel) 869 870 local_conv = keras.backend.local_conv(inputs, 871 kernel, 872 kernel_sizes, 873 strides, 874 output_shape, 875 'channels_last') 876 if len(output_shape) == 1: 877 local_conv_dim = keras.backend.local_conv1d(inputs, 878 kernel, 879 kernel_sizes, 880 strides, 881 'channels_last') 882 else: 883 local_conv_dim = keras.backend.local_conv2d(inputs, 884 kernel, 885 kernel_sizes, 886 strides, 887 output_shape, 888 'channels_last') 889 890 local_conv = keras.backend.eval(local_conv) 891 local_conv_dim = keras.backend.eval(local_conv_dim) 892 893 self.assertAllCloseAccordingToType(local_conv, local_conv_dim) 894 895 def test_conv2d(self): 896 val = np.random.random((10, 4, 10, 10)) 897 x = keras.backend.variable(val) 898 kernel_val = np.random.random((3, 3, 4, 5)) 899 k = keras.backend.variable(kernel_val) 900 y = keras.backend.conv2d(x, k, 901 padding='valid', data_format='channels_first') 902 self.assertEqual(y.shape.as_list(), [10, 5, 8, 8]) 903 904 val = np.random.random((10, 10, 10, 4)) 905 x = keras.backend.variable(val) 906 y = keras.backend.conv2d(x, k, strides=(1, 1), 907 padding='valid', data_format='channels_last') 908 self.assertEqual(y.shape.as_list(), [10, 8, 8, 5]) 909 910 val = np.random.random((10, 10, 10, 4)) 911 x = keras.backend.variable(val) 912 y = keras.backend.conv2d(x, k, strides=(1, 1), 913 padding='same', data_format='channels_last') 914 self.assertEqual(y.shape.as_list(), [10, 10, 10, 5]) 915 916 val = np.random.random((10, 10, 10, 4)) 917 x = keras.backend.variable(val) 918 y = keras.backend.conv2d(x, k, strides=(2, 2), 919 padding='same', data_format='channels_last') 920 self.assertEqual(y.shape.as_list(), [10, 5, 5, 5]) 921 with self.assertRaises(ValueError): 922 y = keras.backend.conv2d(x, k, (2, 2), 923 padding='other', data_format='channels_last') 924 with self.assertRaises(ValueError): 925 y = keras.backend.conv2d(x, k, (2, 2), 926 data_format='other') 927 with self.assertRaises(ValueError): 928 y = keras.backend.conv2d(x, k, (2, 2, 2)) 929 930 def test_separable_conv2d(self): 931 val = np.random.random((10, 4, 10, 10)) 932 x = keras.backend.variable(val) 933 depthwise_kernel_val = np.random.random((3, 3, 4, 1)) 934 pointwise_kernel_val = np.random.random((1, 1, 4, 5)) 935 dk = keras.backend.variable(depthwise_kernel_val) 936 pk = keras.backend.variable(pointwise_kernel_val) 937 y = keras.backend.separable_conv2d( 938 x, dk, pk, padding='valid', data_format='channels_first') 939 self.assertEqual(y.shape.as_list(), [10, 5, 8, 8]) 940 941 val = np.random.random((10, 10, 10, 4)) 942 x = keras.backend.variable(val) 943 y = keras.backend.separable_conv2d( 944 x, dk, pk, strides=(1, 1), padding='valid', data_format='channels_last') 945 self.assertEqual(y.shape.as_list(), [10, 8, 8, 5]) 946 947 val = np.random.random((10, 10, 10, 4)) 948 x = keras.backend.variable(val) 949 y = keras.backend.separable_conv2d( 950 x, dk, pk, strides=(1, 1), padding='same', data_format='channels_last') 951 self.assertEqual(y.shape.as_list(), [10, 10, 10, 5]) 952 953 val = np.random.random((10, 10, 10, 4)) 954 x = keras.backend.variable(val) 955 y = keras.backend.separable_conv2d( 956 x, dk, pk, strides=(2, 2), padding='same', data_format='channels_last') 957 self.assertEqual(y.shape.as_list(), [10, 5, 5, 5]) 958 with self.assertRaises(ValueError): 959 y = keras.backend.separable_conv2d( 960 x, dk, pk, (2, 2), padding='other', data_format='channels_last') 961 with self.assertRaises(ValueError): 962 y = keras.backend.separable_conv2d( 963 x, dk, pk, (2, 2), data_format='other') 964 with self.assertRaises(ValueError): 965 y = keras.backend.separable_conv2d(x, dk, pk, (2, 2, 2)) 966 967 def test_conv3d(self): 968 val = np.random.random((10, 4, 10, 10, 10)) 969 x = keras.backend.variable(val) 970 kernel_val = np.random.random((3, 3, 3, 4, 5)) 971 k = keras.backend.variable(kernel_val) 972 y = keras.backend.conv3d(x, k, 973 padding='valid', data_format='channels_first') 974 self.assertEqual(y.shape.as_list(), [10, 5, 8, 8, 8]) 975 976 val = np.random.random((10, 10, 10, 10, 4)) 977 x = keras.backend.variable(val) 978 y = keras.backend.conv3d(x, k, strides=(1, 1, 1), 979 padding='valid', data_format='channels_last') 980 self.assertEqual(y.shape.as_list(), [10, 8, 8, 8, 5]) 981 982 val = np.random.random((10, 10, 10, 10, 4)) 983 x = keras.backend.variable(val) 984 y = keras.backend.conv3d(x, k, strides=(1, 1, 1), 985 padding='same', data_format='channels_last') 986 self.assertEqual(y.shape.as_list(), [10, 10, 10, 10, 5]) 987 988 val = np.random.random((10, 10, 10, 10, 4)) 989 x = keras.backend.variable(val) 990 y = keras.backend.conv3d(x, k, strides=(2, 2, 2), 991 padding='same', data_format='channels_last') 992 self.assertEqual(y.shape.as_list(), [10, 5, 5, 5, 5]) 993 with self.assertRaises(ValueError): 994 y = keras.backend.conv3d(x, k, (2, 2, 2), 995 padding='other', data_format='channels_last') 996 with self.assertRaises(ValueError): 997 y = keras.backend.conv3d(x, k, (2, 2, 2), 998 data_format='other') 999 with self.assertRaises(ValueError): 1000 y = keras.backend.conv3d(x, k, (2, 2)) 1001 1002 def test_rnn(self): 1003 # implement a simple RNN 1004 num_samples = 4 1005 input_dim = 5 1006 output_dim = 3 1007 timesteps = 6 1008 1009 input_val = np.random.random( 1010 (num_samples, timesteps, input_dim)).astype(np.float32) 1011 init_state_val = np.random.random( 1012 (num_samples, output_dim)).astype(np.float32) 1013 w_i_val = np.random.random((input_dim, output_dim)).astype(np.float32) 1014 w_o_val = np.random.random((output_dim, output_dim)).astype(np.float32) 1015 np_mask = np.random.randint(2, size=(num_samples, timesteps)) 1016 1017 def rnn_step_fn(): 1018 w_i = keras.backend.variable(w_i_val) 1019 w_o = keras.backend.variable(w_o_val) 1020 1021 def step_function(x, states): 1022 assert len(states) == 1 1023 prev_output = states[0] 1024 output = keras.backend.dot(x, w_i) + keras.backend.dot(prev_output, w_o) 1025 return output, [output] 1026 1027 return step_function 1028 1029 # test default setup 1030 last_output_list = [[], [], [], [], [], []] 1031 outputs_list = [[], [], [], [], [], []] 1032 state_list = [[], [], [], [], [], []] 1033 1034 rnn_fn = rnn_step_fn() 1035 inputs = keras.backend.variable(input_val) 1036 initial_states = [keras.backend.variable(init_state_val)] 1037 mask = keras.backend.variable(np_mask) 1038 1039 kwargs_list = [ 1040 {'go_backwards': False, 'mask': None}, 1041 {'go_backwards': False, 'mask': None, 'unroll': True}, 1042 {'go_backwards': True, 'mask': None}, 1043 {'go_backwards': True, 'mask': None, 'unroll': True}, 1044 {'go_backwards': False, 'mask': mask}, 1045 {'go_backwards': False, 'mask': mask, 'unroll': True}, 1046 ] 1047 for i, kwargs in enumerate(kwargs_list): 1048 last_output, outputs, new_states = keras.backend.rnn(rnn_fn, inputs, 1049 initial_states, 1050 **kwargs) 1051 # check static shape inference 1052 self.assertEqual(last_output.shape.as_list(), [num_samples, output_dim]) 1053 self.assertEqual(outputs.shape.as_list(), 1054 [num_samples, timesteps, output_dim]) 1055 for state in new_states: 1056 self.assertEqual(state.shape.as_list(), [num_samples, output_dim]) 1057 1058 last_output_list[i].append(keras.backend.eval(last_output)) 1059 outputs_list[i].append(keras.backend.eval(outputs)) 1060 self.assertLen(new_states, 1) 1061 state_list[i].append(keras.backend.eval(new_states[0])) 1062 1063 def assert_list_pairwise(z_list, atol=1e-05): 1064 for (z1, z2) in zip(z_list[1:], z_list[:-1]): 1065 self.assertAllClose(z1, z2, atol=atol) 1066 1067 assert_list_pairwise(last_output_list[0], atol=1e-04) 1068 assert_list_pairwise(outputs_list[0], atol=1e-04) 1069 assert_list_pairwise(state_list[0], atol=1e-04) 1070 assert_list_pairwise(last_output_list[2], atol=1e-04) 1071 assert_list_pairwise(outputs_list[2], atol=1e-04) 1072 assert_list_pairwise(state_list[2], atol=1e-04) 1073 1074 for l, u_l in zip(last_output_list[0], last_output_list[1]): 1075 self.assertAllClose(l, u_l, atol=1e-04) 1076 1077 for o, u_o in zip(outputs_list[0], outputs_list[1]): 1078 self.assertAllClose(o, u_o, atol=1e-04) 1079 1080 for s, u_s in zip(state_list[0], state_list[1]): 1081 self.assertAllClose(s, u_s, atol=1e-04) 1082 1083 for b_l, b_u_l in zip(last_output_list[2], last_output_list[3]): 1084 self.assertAllClose(b_l, b_u_l, atol=1e-04) 1085 1086 for b_o, b_u_o in zip(outputs_list[2], outputs_list[3]): 1087 self.assertAllClose(b_o, b_u_o, atol=1e-04) 1088 1089 for b_s, b_u_s in zip(state_list[2], state_list[3]): 1090 self.assertAllClose(b_s, b_u_s, atol=1e-04) 1091 1092 def test_rnn_additional_states(self): 1093 # implement a simple RNN 1094 num_samples = 4 1095 input_dim = 5 1096 output_dim = 3 1097 timesteps = 6 1098 1099 input_val = np.random.random( 1100 (num_samples, timesteps, input_dim)).astype(np.float32) 1101 init_state_val = np.random.random( 1102 (num_samples, output_dim)).astype(np.float32) 1103 w_i_val = np.random.random((input_dim, output_dim)).astype(np.float32) 1104 w_o_val = np.random.random((output_dim, output_dim)).astype(np.float32) 1105 np_mask = np.random.randint(2, size=(num_samples, timesteps)) 1106 1107 def rnn_step_fn(): 1108 w_i = keras.backend.variable(w_i_val) 1109 w_o = keras.backend.variable(w_o_val) 1110 1111 def step_function(x, states): 1112 assert len(states) == 2 1113 prev_output = states[0] 1114 output = keras.backend.dot(x, w_i) + keras.backend.dot(prev_output, w_o) 1115 return output, [output, 1116 keras.backend.concatenate([output, output], axis=-1)] 1117 1118 return step_function 1119 1120 # test default setup 1121 last_output_list = [[], [], [], [], [], []] 1122 outputs_list = [[], [], [], [], [], []] 1123 state_list = [[], [], [], [], [], []] 1124 additional_state_list = [[], [], [], [], [], []] 1125 1126 rnn_fn = rnn_step_fn() 1127 inputs = keras.backend.variable(input_val) 1128 initial_states = [ 1129 keras.backend.variable(init_state_val), 1130 ops.convert_to_tensor( 1131 np.concatenate([init_state_val, init_state_val], axis=-1)) 1132 ] 1133 mask = keras.backend.variable(np_mask) 1134 1135 kwargs_list = [ 1136 {'go_backwards': False, 'mask': None}, 1137 {'go_backwards': False, 'mask': None, 'unroll': True}, 1138 {'go_backwards': True, 'mask': None}, 1139 {'go_backwards': True, 'mask': None, 'unroll': True}, 1140 {'go_backwards': False, 'mask': mask}, 1141 {'go_backwards': False, 'mask': mask, 'unroll': True}, 1142 ] 1143 for i, kwargs in enumerate(kwargs_list): 1144 last_output, outputs, new_states = keras.backend.rnn(rnn_fn, inputs, 1145 initial_states, 1146 **kwargs) 1147 # check static shape inference 1148 self.assertEqual(last_output.shape.as_list(), [num_samples, output_dim]) 1149 self.assertEqual(outputs.shape.as_list(), 1150 [num_samples, timesteps, output_dim]) 1151 # for state in new_states: 1152 # self.assertEqual(state.shape.as_list(), 1153 # [num_samples, output_dim]) 1154 self.assertEqual(new_states[0].shape.as_list(), [num_samples, output_dim]) 1155 self.assertEqual(new_states[1].shape.as_list(), 1156 [num_samples, 2 * output_dim]) 1157 1158 last_output_list[i].append(keras.backend.eval(last_output)) 1159 outputs_list[i].append(keras.backend.eval(outputs)) 1160 self.assertLen(new_states, 2) 1161 state_list[i].append(keras.backend.eval(new_states[0])) 1162 additional_state_list[i].append(keras.backend.eval(new_states[1])) 1163 1164 def assert_list_pairwise(z_list, atol=1e-05): 1165 for (z1, z2) in zip(z_list[1:], z_list[:-1]): 1166 self.assertAllClose(z1, z2, atol=atol) 1167 1168 assert_list_pairwise(last_output_list[0], atol=1e-04) 1169 assert_list_pairwise(outputs_list[0], atol=1e-04) 1170 assert_list_pairwise(state_list[0], atol=1e-04) 1171 assert_list_pairwise(additional_state_list[0], atol=1e-04) 1172 assert_list_pairwise(last_output_list[2], atol=1e-04) 1173 assert_list_pairwise(outputs_list[2], atol=1e-04) 1174 assert_list_pairwise(state_list[2], atol=1e-04) 1175 assert_list_pairwise(additional_state_list[2], atol=1e-04) 1176 1177 for l, u_l in zip(last_output_list[0], last_output_list[1]): 1178 self.assertAllClose(l, u_l, atol=1e-04) 1179 1180 for o, u_o in zip(outputs_list[0], outputs_list[1]): 1181 self.assertAllClose(o, u_o, atol=1e-04) 1182 1183 for s, u_s in zip(state_list[0], state_list[1]): 1184 self.assertAllClose(s, u_s, atol=1e-04) 1185 1186 for s, u_s in zip(additional_state_list[0], additional_state_list[1]): 1187 self.assertAllClose(s, u_s, atol=1e-04) 1188 1189 for b_l, b_u_l in zip(last_output_list[2], last_output_list[3]): 1190 self.assertAllClose(b_l, b_u_l, atol=1e-04) 1191 1192 for b_o, b_u_o in zip(outputs_list[2], outputs_list[3]): 1193 self.assertAllClose(b_o, b_u_o, atol=1e-04) 1194 1195 for b_s, b_u_s in zip(state_list[2], state_list[3]): 1196 self.assertAllClose(b_s, b_u_s, atol=1e-04) 1197 1198 for s, u_s in zip(additional_state_list[2], additional_state_list[3]): 1199 self.assertAllClose(s, u_s, atol=1e-04) 1200 1201 def test_rnn_output_and_state_masking_independent(self): 1202 num_samples = 2 1203 num_timesteps = 4 1204 state_and_io_size = 2 1205 mask_last_num_timesteps = 2 # for second sample only 1206 1207 # a step function that just outputs inputs, 1208 # but increments states +1 per timestep 1209 def step_function(inputs, states): 1210 return inputs, [s + 1 for s in states] 1211 1212 inputs_vals = np.random.random((num_samples, num_timesteps, 1213 state_and_io_size)) 1214 initial_state_vals = np.random.random((num_samples, state_and_io_size)) 1215 # masking of two last timesteps for second sample only 1216 mask_vals = np.ones((num_samples, num_timesteps)) 1217 mask_vals[1, -mask_last_num_timesteps:] = 0 1218 1219 # outputs expected to be same as inputs for the first sample 1220 expected_outputs = inputs_vals.copy() 1221 # but for the second sample all outputs in masked region should be the same 1222 # as last output before masked region 1223 expected_outputs[1, -mask_last_num_timesteps:] = \ 1224 expected_outputs[1, -(mask_last_num_timesteps + 1)] 1225 1226 expected_last_state = initial_state_vals.copy() 1227 # first state should be incremented for every timestep (no masking) 1228 expected_last_state[0] += num_timesteps 1229 # second state should not be incremented for last two timesteps 1230 expected_last_state[1] += (num_timesteps - mask_last_num_timesteps) 1231 1232 # verify same expected output for `unroll=true/false` 1233 inputs = keras.backend.variable(inputs_vals) 1234 initial_states = [keras.backend.variable(initial_state_vals)] 1235 mask = keras.backend.variable(mask_vals) 1236 for unroll in [True, False]: 1237 _, outputs, last_states = keras.backend.rnn( 1238 step_function, 1239 inputs, 1240 initial_states, 1241 mask=mask, 1242 unroll=unroll, 1243 input_length=num_timesteps if unroll else None) 1244 1245 self.assertAllClose(keras.backend.eval(outputs), expected_outputs) 1246 self.assertAllClose( 1247 keras.backend.eval(last_states[0]), expected_last_state) 1248 1249 def test_rnn_output_num_dim_larger_than_2_masking(self): 1250 num_samples = 3 1251 num_timesteps = 4 1252 num_features = 5 1253 1254 def step_function(inputs, states): 1255 outputs = keras.backend.tile(keras.backend.expand_dims(inputs), [1, 1, 2]) 1256 return outputs, [keras.backend.identity(s) for s in states] 1257 # Note: cannot just return states (which can be a problem) -> 1258 # tensorflow/python/ops/resource_variable_ops.py", line 824, in set_shape 1259 # NotImplementedError: ResourceVariable does not implement set_shape() 1260 1261 inputs_vals = np.random.random((num_samples, num_timesteps, num_features)) 1262 initial_state_vals = np.random.random((num_samples, 6)) 1263 mask_vals = np.ones((num_samples, num_timesteps)) 1264 mask_vals[-1, -1] = 0 # final timestep masked for last sample 1265 1266 expected_outputs = np.repeat(inputs_vals[..., None], repeats=2, axis=-1) 1267 # for the last sample, the final timestep (in masked region) should be the 1268 # same as the second to final output (before masked region) 1269 expected_outputs[-1, -1] = expected_outputs[-1, -2] 1270 1271 inputs = keras.backend.variable(inputs_vals) 1272 initial_states = [keras.backend.variable(initial_state_vals)] 1273 mask = keras.backend.variable(mask_vals) 1274 for unroll in [True, False]: 1275 _, outputs, _ = keras.backend.rnn( 1276 step_function, 1277 inputs, 1278 initial_states, 1279 mask=mask, 1280 unroll=unroll, 1281 input_length=num_timesteps if unroll else None) 1282 1283 self.assertAllClose(keras.backend.eval(outputs), expected_outputs) 1284 1285 def test_rnn_state_num_dim_larger_than_2_masking(self): 1286 num_samples = 3 1287 num_timesteps = 4 1288 1289 def step_function(inputs, states): 1290 return inputs, [s + 1 for s in states] 1291 1292 inputs_vals = np.random.random((num_samples, num_timesteps, 5)) 1293 initial_state_vals = np.random.random((num_samples, 6, 7)) 1294 mask_vals = np.ones((num_samples, num_timesteps)) 1295 mask_vals[0, -2:] = 0 # final two timesteps masked for first sample 1296 1297 expected_last_state = initial_state_vals.copy() 1298 expected_last_state[0] += (num_timesteps - 2) 1299 expected_last_state[1:] += num_timesteps 1300 1301 inputs = keras.backend.variable(inputs_vals) 1302 initial_states = [keras.backend.variable(initial_state_vals)] 1303 mask = keras.backend.variable(mask_vals) 1304 for unroll in [True, False]: 1305 _, _, last_states = keras.backend.rnn( 1306 step_function, 1307 inputs, 1308 initial_states, 1309 mask=mask, 1310 unroll=unroll, 1311 input_length=num_timesteps if unroll else None) 1312 1313 self.assertAllClose( 1314 keras.backend.eval(last_states[0]), expected_last_state) 1315 1316 def test_normalize_batch_in_training(self): 1317 val = np.random.random((10, 3, 10, 10)) 1318 x = keras.backend.variable(val) 1319 reduction_axes = (0, 2, 3) 1320 1321 g_val = np.random.random((3,)) 1322 b_val = np.random.random((3,)) 1323 gamma = keras.backend.variable(g_val) 1324 beta = keras.backend.variable(b_val) 1325 normed, mean, var = keras.backend.normalize_batch_in_training( 1326 x, gamma, beta, reduction_axes, epsilon=1e-3) 1327 self.assertEqual(normed.shape.as_list(), [10, 3, 10, 10]) 1328 self.assertEqual(mean.shape.as_list(), [ 1329 3, 1330 ]) 1331 self.assertEqual(var.shape.as_list(), [ 1332 3, 1333 ]) 1334 1335 # case: gamma=None 1336 gamma = None 1337 normed, mean, var = keras.backend.normalize_batch_in_training( 1338 x, gamma, beta, reduction_axes, epsilon=1e-3) 1339 self.assertEqual(normed.shape.as_list(), [10, 3, 10, 10]) 1340 self.assertEqual(mean.shape.as_list(), [ 1341 3, 1342 ]) 1343 self.assertEqual(var.shape.as_list(), [ 1344 3, 1345 ]) 1346 1347 # case: beta=None 1348 beta = None 1349 normed, mean, var = keras.backend.normalize_batch_in_training( 1350 x, gamma, beta, reduction_axes, epsilon=1e-3) 1351 self.assertEqual(normed.shape.as_list(), [10, 3, 10, 10]) 1352 self.assertEqual(mean.shape.as_list(), [ 1353 3, 1354 ]) 1355 self.assertEqual(var.shape.as_list(), [ 1356 3, 1357 ]) 1358 1359 def test_dropout(self): 1360 inputs = array_ops.ones((200, 200)) 1361 outputs = keras.backend.dropout(inputs, 0.2) 1362 outputs_val = keras.backend.eval(outputs) 1363 self.assertEqual(np.min(outputs_val), 0) 1364 self.assertAllClose(np.count_nonzero(outputs_val), 32000, atol=1000) 1365 # Test noise shape 1366 outputs = keras.backend.dropout(inputs, 0.2, noise_shape=(200, 1)) 1367 outputs_val = keras.backend.eval(outputs) 1368 self.assertAllClose(outputs_val[2, :], outputs_val[3, :], atol=1e-5) 1369 1370 1371 @test_util.run_all_in_graph_and_eager_modes 1372 class TestCTC(test.TestCase): 1373 1374 def test_ctc_decode(self): 1375 depth = 6 1376 seq_len_0 = 5 1377 input_prob_matrix_0 = np.asarray( 1378 [[0.30999, 0.309938, 0.0679938, 0.0673362, 0.0708352, 0.173908], 1379 [0.215136, 0.439699, 0.0370931, 0.0393967, 0.0381581, 0.230517], 1380 [0.199959, 0.489485, 0.0233221, 0.0251417, 0.0233289, 0.238763], 1381 [0.279611, 0.452966, 0.0204795, 0.0209126, 0.0194803, 0.20655], 1382 [0.51286, 0.288951, 0.0243026, 0.0220788, 0.0219297, 0.129878], 1383 # Random entry added in at time=5 1384 [0.155251, 0.164444, 0.173517, 0.176138, 0.169979, 0.160671]], 1385 dtype=np.float32) 1386 1387 # len max_time_steps array of batch_size x depth matrices 1388 inputs = ([input_prob_matrix_0[t, :][np.newaxis, :] 1389 for t in range(seq_len_0)] + # Pad to max_time_steps = 8 1390 2 * [np.zeros((1, depth), dtype=np.float32)]) 1391 1392 inputs = keras.backend.variable(np.asarray(inputs).transpose((1, 0, 2))) 1393 1394 # batch_size length vector of sequence_lengths 1395 input_length = keras.backend.variable( 1396 np.array([seq_len_0], dtype=np.int32)) 1397 # batch_size length vector of negative log probabilities 1398 log_prob_truth = np.array([ 1399 -3.5821197, # output beam 0 1400 -3.777835 # output beam 1 1401 ], np.float32)[np.newaxis, :] 1402 1403 decode_truth = [np.array([1, 0]), np.array([0, 1, 0])] 1404 beam_width = 2 1405 top_paths = 2 1406 1407 decode_pred_tf, log_prob_pred_tf = keras.backend.ctc_decode( 1408 inputs, 1409 input_length, 1410 greedy=False, 1411 beam_width=beam_width, 1412 top_paths=top_paths) 1413 1414 self.assertEqual(len(decode_pred_tf), top_paths) 1415 log_prob_pred = keras.backend.eval(log_prob_pred_tf) 1416 for i in range(top_paths): 1417 self.assertTrue( 1418 np.alltrue( 1419 decode_truth[i] == keras.backend.eval(decode_pred_tf[i]))) 1420 self.assertAllClose(log_prob_truth, log_prob_pred) 1421 1422 @test_util.run_v1_only('b/120545219') 1423 def test_ctc_batch_cost(self): 1424 with self.cached_session(): 1425 label_lens = np.expand_dims(np.asarray([5, 4]), 1) 1426 input_lens = np.expand_dims(np.asarray([5, 5]), 1) # number of timesteps 1427 loss_log_probs = [3.34211, 5.42262] 1428 1429 # dimensions are batch x time x categories 1430 labels = np.asarray([[0, 1, 2, 1, 0], [0, 1, 1, 0, -1]]) 1431 inputs = np.asarray( 1432 [[[0.633766, 0.221185, 0.0917319, 0.0129757, 0.0142857, 0.0260553], 1433 [0.111121, 0.588392, 0.278779, 0.0055756, 0.00569609, 0.010436], 1434 [0.0357786, 0.633813, 0.321418, 0.00249248, 0.00272882, 0.0037688], 1435 [0.0663296, 0.643849, 0.280111, 0.00283995, 0.0035545, 0.00331533], 1436 [0.458235, 0.396634, 0.123377, 0.00648837, 0.00903441, 0.00623107]], 1437 [[0.30176, 0.28562, 0.0831517, 0.0862751, 0.0816851, 0.161508], 1438 [0.24082, 0.397533, 0.0557226, 0.0546814, 0.0557528, 0.19549], 1439 [0.230246, 0.450868, 0.0389607, 0.038309, 0.0391602, 0.202456], 1440 [0.280884, 0.429522, 0.0326593, 0.0339046, 0.0326856, 0.190345], 1441 [0.423286, 0.315517, 0.0338439, 0.0393744, 0.0339315, 0.154046]]], 1442 dtype=np.float32) 1443 1444 labels = keras.backend.variable(labels, dtype='int32') 1445 inputs = keras.backend.variable(inputs, dtype='float32') 1446 input_lens = keras.backend.variable(input_lens, dtype='int32') 1447 label_lens = keras.backend.variable(label_lens, dtype='int32') 1448 res = keras.backend.eval( 1449 keras.backend.ctc_batch_cost(labels, inputs, input_lens, label_lens)) 1450 self.assertAllClose(res[:, 0], loss_log_probs, atol=1e-05) 1451 1452 # test when batch_size = 1, that is, one sample only 1453 ref = [3.34211] 1454 input_lens = np.expand_dims(np.asarray([5]), 1) 1455 label_lens = np.expand_dims(np.asarray([5]), 1) 1456 1457 labels = np.asarray([[0, 1, 2, 1, 0]]) 1458 inputs = np.asarray( 1459 [[[0.633766, 0.221185, 0.0917319, 0.0129757, 0.0142857, 0.0260553], [ 1460 0.111121, 0.588392, 0.278779, 0.0055756, 0.00569609, 0.010436 1461 ], [0.0357786, 0.633813, 0.321418, 0.00249248, 0.00272882, 0.0037688], 1462 [0.0663296, 0.643849, 0.280111, 0.00283995, 0.0035545, 0.00331533], 1463 [0.458235, 0.396634, 0.123377, 0.00648837, 0.00903441, 0.00623107]] 1464 ], 1465 dtype=np.float32) 1466 1467 k_labels = keras.backend.variable(labels, dtype='int32') 1468 k_inputs = keras.backend.variable(inputs, dtype='float32') 1469 k_input_lens = keras.backend.variable(input_lens, dtype='int32') 1470 k_label_lens = keras.backend.variable(label_lens, dtype='int32') 1471 res = keras.backend.eval( 1472 keras.backend.ctc_batch_cost(k_labels, k_inputs, k_input_lens, 1473 k_label_lens)) 1474 self.assertAllClose(res[:, 0], ref, atol=1e-05) 1475 1476 1477 @test_util.run_all_in_graph_and_eager_modes 1478 class TestRandomOps(test.TestCase): 1479 1480 def test_random_binomial(self): 1481 np.random.seed(123) 1482 x = keras.backend.random_binomial((1000, 1000), p=0.5) 1483 self.assertAllClose(np.mean(keras.backend.eval(x)), 0.5, atol=0.1) 1484 1485 def test_truncated_normal(self): 1486 np.random.seed(123) 1487 x = keras.backend.truncated_normal((1000, 1000), mean=0.0, stddev=1.0) 1488 y = keras.backend.eval(x) 1489 self.assertAllClose(np.mean(y), 0., atol=0.1) 1490 self.assertAllClose(np.std(y), 0.88, atol=0.1) 1491 self.assertAllClose(np.max(y), 2., atol=0.1) 1492 self.assertAllClose(np.min(y), -2., atol=0.1) 1493 1494 def test_string_input(self): 1495 seq = keras.Sequential([ 1496 keras.layers.InputLayer(input_shape=(1,), dtype=dtypes.string), 1497 keras.layers.Lambda(lambda x: x[0]) 1498 ]) 1499 preds = seq.predict([['tensorflow eager']]) 1500 self.assertEqual(preds.shape, (1,)) 1501 1502 1503 class BackendGraphTests(test.TestCase): 1504 1505 @test_util.run_deprecated_v1 1506 def test_is_placeholder(self): 1507 x = keras.backend.placeholder(shape=(1,)) 1508 self.assertEqual(keras.backend.is_placeholder(x), True) 1509 # Test with TF placeholder 1510 x = keras.backend.array_ops.placeholder(dtype='float32', shape=(1,)) 1511 self.assertEqual(keras.backend.is_placeholder(x), True) 1512 x = keras.backend.variable(1) 1513 self.assertEqual(keras.backend.is_placeholder(x), False) 1514 1515 @test_util.run_in_graph_and_eager_modes 1516 def test_function_basics(self): 1517 x1 = keras.backend.placeholder(shape=(), dtype='float32') 1518 x2 = keras.backend.placeholder(shape=(), dtype='int32') 1519 v = keras.backend.variable(10.) 1520 with keras.backend.get_graph().as_default(): 1521 y1 = x1 + keras.backend.cast(x2, 'float32') + v 1522 y2 = x1 * keras.backend.cast(x2, 'float32') 1523 with ops.control_dependencies([y1]): 1524 u = keras.backend.update(v, 5.) 1525 f = keras.backend.function([x1, x2], [y1, y2], updates=[u]) 1526 output_values = f([2, 3]) 1527 self.assertEqual(output_values, [15., 6.]) 1528 self.assertEqual(keras.backend.eval(v), 5.) 1529 1530 @test_util.run_in_graph_and_eager_modes 1531 def test_function_placeholder_with_default(self): 1532 with keras.backend.get_graph().as_default(): 1533 x1 = array_ops.placeholder_with_default( 1534 np.array(2., dtype='float32'), shape=()) 1535 x2 = array_ops.placeholder_with_default( 1536 np.array(3, dtype='int32'), shape=()) 1537 y1 = x1 + keras.backend.cast(x2, 'float32') 1538 y2 = x1 * keras.backend.cast(x2, 'float32') 1539 f = keras.backend.function([x1, x2], [y1, y2]) 1540 output_values = f([4, 5]) 1541 self.assertEqual(output_values, [9., 20.]) 1542 output_values = f([None, None]) 1543 self.assertEqual(output_values, [5., 6.]) 1544 1545 @test_util.run_deprecated_v1 1546 def test_function_tf_feed_symbols(self): 1547 # Test Keras backend functions with TF tensor inputs. 1548 with self.cached_session(): 1549 # Test feeding a resource variable to `function`. 1550 x1 = keras.backend.placeholder(shape=()) 1551 x2 = keras.backend.placeholder(shape=()) 1552 lr = keras.backend.learning_phase() # Include a placeholder_with_default. 1553 1554 y1 = keras.backend.variable(10.) 1555 y2 = 3 1556 1557 f = keras.backend.function( 1558 inputs=[x1, x2, lr], 1559 outputs=[x1 + 1, keras.backend.in_train_phase(x2 + 2, x2 - 1)]) 1560 outs = f([y1, y2, None]) # Use default learning_phase value. 1561 self.assertEqual(outs, [11., 2.]) 1562 outs = f([y1, y2, 1]) # Set learning phase value. 1563 self.assertEqual(outs, [11., 5.]) 1564 1565 # Test triggering a callable refresh by changing the input. 1566 y3 = keras.backend.constant(20.) # Test with tensor 1567 outs = f([y3, y2, None]) 1568 self.assertEqual(outs, [21., 2.]) 1569 1570 y4 = 4 # Test with non-symbol 1571 outs = f([y4, y2, None]) 1572 self.assertEqual(outs, [5., 2.]) 1573 1574 # Test with a different dtype 1575 y5 = keras.backend.constant(10., dtype='float64') 1576 outs = f([y5, y2, None]) 1577 self.assertEqual(outs, [11., 2.]) 1578 1579 @test_util.run_deprecated_v1 1580 def test_function_tf_fetches(self): 1581 # Additional operations can be passed to tf.Session().run() via its 1582 # `fetches` arguments. In contrast to `updates` argument of 1583 # keras.backend.function() these do not have control dependency on `outputs` 1584 # so they can run in parallel. Also they should not contribute to output of 1585 # keras.backend.function(). 1586 with self.cached_session(): 1587 x = keras.backend.variable(0.) 1588 y = keras.backend.variable(0.) 1589 x_placeholder = keras.backend.placeholder(shape=()) 1590 y_placeholder = keras.backend.placeholder(shape=()) 1591 1592 f = keras.backend.function( 1593 inputs=[x_placeholder, y_placeholder], 1594 outputs=[x_placeholder + y_placeholder], 1595 updates=[(x, x_placeholder + 1.)], 1596 fetches=[keras.backend.update(y, 5.)]) 1597 output = f([10., 20.]) 1598 self.assertEqual(output, [30.]) 1599 self.assertEqual(keras.backend.get_session().run(fetches=[x, y]), 1600 [11., 5.]) 1601 1602 @test_util.run_deprecated_v1 1603 def test_function_tf_feed_dict(self): 1604 # Additional substitutions can be passed to `tf.Session().run()` via its 1605 # `feed_dict` arguments. Note that the feed_dict is passed once in the 1606 # constructor but we can modify the values in the dictionary. Through 1607 # this feed_dict we can provide additional substitutions besides Keras 1608 # inputs. 1609 with self.cached_session(): 1610 x = keras.backend.variable(0.) 1611 y = keras.backend.variable(0.) 1612 x_placeholder = keras.backend.placeholder(shape=()) 1613 y_placeholder = keras.backend.placeholder(shape=()) 1614 1615 feed_dict = {y_placeholder: 3.} 1616 fetches = [keras.backend.update(y, y_placeholder * 10.)] 1617 f = keras.backend.function( 1618 inputs=[x_placeholder], 1619 outputs=[x_placeholder + 1.], 1620 updates=[(x, x_placeholder + 10.)], 1621 feed_dict=feed_dict, 1622 fetches=fetches) 1623 output = f([10.]) 1624 self.assertEqual(output, [11.]) 1625 self.assertEqual(keras.backend.get_session().run(fetches=[x, y]), 1626 [20., 30.]) 1627 1628 # updated value in feed_dict will be modified within the K.function() 1629 feed_dict[y_placeholder] = 4. 1630 output = f([20.]) 1631 self.assertEqual(output, [21.]) 1632 self.assertEqual(keras.backend.get_session().run(fetches=[x, y]), 1633 [30., 40.]) 1634 1635 @test_util.run_deprecated_v1 1636 def test_function_tf_run_options_with_run_metadata(self): 1637 with self.cached_session(): 1638 x_placeholder = keras.backend.placeholder(shape=()) 1639 y_placeholder = keras.backend.placeholder(shape=()) 1640 1641 run_options = config_pb2.RunOptions(output_partition_graphs=True) 1642 run_metadata = config_pb2.RunMetadata() 1643 # enable run_options. 1644 f = keras.backend.function( 1645 inputs=[x_placeholder, y_placeholder], 1646 outputs=[x_placeholder + y_placeholder], 1647 options=run_options, 1648 run_metadata=run_metadata) 1649 output = f([10., 20.]) 1650 self.assertEqual(output, [30.]) 1651 self.assertGreater(len(run_metadata.partition_graphs), 0) 1652 # disable run_options. 1653 f1 = keras.backend.function( 1654 inputs=[x_placeholder, y_placeholder], 1655 outputs=[x_placeholder + y_placeholder], 1656 run_metadata=run_metadata) 1657 output1 = f1([10., 20.]) 1658 self.assertEqual(output1, [30.]) 1659 self.assertEqual(len(run_metadata.partition_graphs), 0) 1660 1661 @test_util.run_deprecated_v1 1662 def test_function_fetch_callbacks(self): 1663 1664 class CallbackStub(object): 1665 1666 def __init__(self): 1667 self.times_called = 0 1668 self.callback_result = 0 1669 1670 def _fetch_callback(self, result): 1671 self.times_called += 1 1672 self.callback_result = result 1673 1674 with self.cached_session(): 1675 callback = CallbackStub() 1676 x_placeholder = keras.backend.placeholder(shape=()) 1677 y_placeholder = keras.backend.placeholder(shape=()) 1678 1679 callback_op = x_placeholder * y_placeholder 1680 1681 f = keras.backend.function( 1682 inputs=[x_placeholder, y_placeholder], 1683 outputs=[x_placeholder + y_placeholder]) 1684 f.fetches.append(callback_op) 1685 f.fetch_callbacks[callback_op] = callback._fetch_callback 1686 1687 _ = f([10., 20.]) 1688 1689 self.assertEqual(callback.times_called, 1) 1690 self.assertEqual(callback.callback_result, 200) 1691 1692 @test_util.run_in_graph_and_eager_modes 1693 def test_function_dict_outputs(self): 1694 x_ph = keras.backend.placeholder(shape=(), name='x') 1695 y_ph = keras.backend.placeholder(shape=(), name='y') 1696 outputs = {'x*y': y_ph * x_ph, 'x*x': x_ph * x_ph} 1697 1698 f = keras.backend.function(inputs=[x_ph, y_ph], outputs=outputs) 1699 x, y = 2., 5. 1700 results = f([x, y]) 1701 1702 self.assertEqual(results['x*y'], 10.) 1703 self.assertEqual(results['x*x'], 4) 1704 1705 @test_util.run_in_graph_and_eager_modes 1706 def test_function_dict_inputs(self): 1707 placeholders = { 1708 'x': keras.backend.placeholder(shape=()), 1709 'y': keras.backend.placeholder(shape=()) 1710 } 1711 outputs = [placeholders['x'] * placeholders['y']] 1712 1713 f = keras.backend.function(inputs=placeholders, outputs=outputs) 1714 results = f({'x': 2., 'y': 3.}) 1715 self.assertEqual(results[0], 6.) 1716 1717 @test_util.run_in_graph_and_eager_modes 1718 def test_function_single_input_output(self): 1719 x_ph = keras.backend.placeholder(shape=(), name='x') 1720 output = x_ph * x_ph 1721 f = keras.backend.function(x_ph, output) 1722 result = f(2.) 1723 self.assertEqual(result, 4.) 1724 1725 def test_placeholder(self): 1726 x = keras.backend.placeholder(shape=(3, 4)) 1727 self.assertEqual(x.shape.as_list(), [3, 4]) 1728 x = keras.backend.placeholder(shape=(3, 4), sparse=True) 1729 self.assertEqual(x.shape.as_list(), [3, 4]) 1730 1731 @test_util.run_deprecated_v1 1732 def test_batch_normalization(self): 1733 # No eager CPU kernel. 1734 g_val = np.random.random((3,)) 1735 b_val = np.random.random((3,)) 1736 gamma = keras.backend.variable(g_val) 1737 beta = keras.backend.variable(b_val) 1738 1739 # 3D NHC case 1740 val = np.random.random((10, 5, 3)) 1741 x = keras.backend.variable(val) 1742 mean, var = nn.moments(x, (0, 1), None, None, False) 1743 normed = keras.backend.batch_normalization( 1744 x, mean, var, beta, gamma, axis=-1, epsilon=1e-3) 1745 self.assertEqual(normed.shape.as_list(), [10, 5, 3]) 1746 1747 # 4D NHWC case 1748 val = np.random.random((10, 5, 5, 3)) 1749 x = keras.backend.variable(val) 1750 mean, var = nn.moments(x, (0, 1, 2), None, None, False) 1751 normed = keras.backend.batch_normalization( 1752 x, mean, var, beta, gamma, axis=-1, epsilon=1e-3) 1753 self.assertEqual(normed.shape.as_list(), [10, 5, 5, 3]) 1754 1755 # 4D NCHW case 1756 val = np.random.random((10, 3, 5, 5)) 1757 x = keras.backend.variable(val) 1758 mean, var = nn.moments(x, (0, 2, 3), None, None, False) 1759 normed = keras.backend.batch_normalization( 1760 x, mean, var, beta, gamma, axis=1, epsilon=1e-3) 1761 self.assertEqual(normed.shape.as_list(), [10, 3, 5, 5]) 1762 1763 def test_get_session_different_graphs(self): 1764 with ops.Graph().as_default(): 1765 x = keras.backend.constant(1) 1766 session = keras.backend.get_session() 1767 self.assertIs(session, keras.backend.get_session((x,))) 1768 self.assertIs(session, keras.backend.get_session()) 1769 with ops.Graph().as_default(): 1770 self.assertIs(session, keras.backend.get_session((x,))) 1771 self.assertIsNot(session, keras.backend.get_session()) 1772 1773 1774 if __name__ == '__main__': 1775 test.main() 1776
