├── Context.sublime-menu ├── Default (Linux).sublime-keymap ├── Default (OSX).sublime-keymap ├── Default (Windows).sublime-keymap ├── Main.sublime-menu ├── README.md ├── helper └── __init__.py ├── message.json └── sublime_tensorflow_autocomplete.py /Context.sublime-menu: -------------------------------------------------------------------------------- 1 | [ 2 | { "command": "tensorflow_doc" } 3 | ] -------------------------------------------------------------------------------- /Default (Linux).sublime-keymap: -------------------------------------------------------------------------------- 1 | [ 2 | { 3 | "keys": ["ctrl+alt+w"], 4 | "command": "tensorflow_doc" 5 | } 6 | ] -------------------------------------------------------------------------------- /Default (OSX).sublime-keymap: -------------------------------------------------------------------------------- 1 | [ 2 | { 3 | "keys": ["ctrl+super+w"], 4 | "command": "tensorflow_doc" 5 | } 6 | ] -------------------------------------------------------------------------------- /Default (Windows).sublime-keymap: -------------------------------------------------------------------------------- 1 | [ 2 | { 3 | "keys": ["ctrl+alt+w"], 4 | "command": "tensorflow_doc" 5 | } 6 | ] -------------------------------------------------------------------------------- /Main.sublime-menu: -------------------------------------------------------------------------------- 1 | [ 2 | { 3 | "caption": "Preferences", 4 | "mnemonic": "n", 5 | "id": "preferences", 6 | "children": 7 | [ 8 | { 9 | "caption": "Package Settings", 10 | "mnemonic": "P", 11 | "id": "package-settings", 12 | "children": 13 | [ 14 | { 15 | "caption": "Sublime Tensorflow", 16 | "children": 17 | [ 18 | { 19 | "command": "open_file", 20 | "args": { 21 | "file": "${packages}/SublimeTensorflow/Default (Windows).sublime-keymap", 22 | "platform": "Windows" 23 | }, 24 | "caption": "Key Bindings – Default" 25 | }, 26 | { 27 | "command": "open_file", 28 | "args": { 29 | "file": "${packages}/SublimeTensorflow/Default (OSX).sublime-keymap", 30 | "platform": "OSX" 31 | }, 32 | "caption": "Key Bindings – Default" 33 | }, 34 | { 35 | "command": "open_file", 36 | "args": { 37 | "file": "${packages}/SublimeTensorflow/Default (Linux).sublime-keymap", 38 | "platform": "Linux" 39 | }, 40 | "caption": "Key Bindings – Default" 41 | }, 42 | { 43 | "command": "open_file", 44 | "args": { 45 | "file": "${packages}/User/Default (Windows).sublime-keymap", 46 | "platform": "Windows" 47 | }, 48 | "caption": "Key Bindings – User" 49 | }, 50 | { 51 | "command": "open_file", 52 | "args": { 53 | "file": "${packages}/User/Default (OSX).sublime-keymap", 54 | "platform": "OSX" 55 | }, 56 | "caption": "Key Bindings – User" 57 | }, 58 | { 59 | "command": "open_file", 60 | "args": { 61 | "file": "${packages}/User/Default (Linux).sublime-keymap", 62 | "platform": "Linux" 63 | }, 64 | "caption": "Key Bindings – User" 65 | }, 66 | { "caption": "-" } 67 | ] 68 | } 69 | ] 70 | } 71 | ] 72 | } 73 | ] -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Sublime Tensorflow 2 | 3 | Tensorflow plugin for Sublime Text editor. 4 | 5 | ![goto doc example](https://user-images.githubusercontent.com/16015833/27678287-fab80c96-5cb4-11e7-8ebd-2818c42776c0.gif "Straight-to-doc example") 6 | 7 | ## Installation ## 8 | 9 | Sublime Tensorflow can be installed: 10 | * Through Sublime Package Control. Package name: *Tensorflow* 11 | * Manually by cloning this repo and copy/paste in a folder into the Sublime Text packages directory. 12 | 13 | ## Usage ## 14 | 15 | Sublime Tensorflow offers you: 16 | * Autocompletion from a list scrapped from official Tensorflow API documentation. 17 | * Shortcut to check the doc by selecting or simply pointing the Tensorflow class/function and: 18 | * Windows & Linux: `ctrl + alt + w` 19 | * OSX: `ctrl + cmd + w` 20 | 21 | ## Configuration ## 22 | 23 | You can change the shortcut by editing the keymap file accessible from *Preferences->Package Settings->Sublime Tensorflow->Settings - User* 24 | 25 | The correct syntax is the following: 26 | 27 | ``` 28 | [ 29 | { 30 | "keys": ["ctrl+alt+w"], 31 | "command": "tfdoc" 32 | } 33 | ] 34 | ``` 35 | 36 | ## Support on Beerpay 37 | Hey dude! Help me out for a couple of :beers:! 38 | 39 | [![Beerpay](https://beerpay.io/baptisteArnaud/Sublime-Tensorflow/badge.svg?style=beer-square)](https://beerpay.io/baptisteArnaud/Sublime-Tensorflow) [![Beerpay](https://beerpay.io/baptisteArnaud/Sublime-Tensorflow/make-wish.svg?style=flat-square)](https://beerpay.io/baptisteArnaud/Sublime-Tensorflow?focus=wish) 40 | -------------------------------------------------------------------------------- /helper/__init__.py: -------------------------------------------------------------------------------- 1 | def get_tf_functions(): 2 | 3 | return [ 4 | "tf.abs()", 5 | "tf.accumulate_n()", 6 | "tf.acos()", 7 | "tf.add()", 8 | "tf.add_check_numerics_ops()", 9 | "tf.add_n()", 10 | "tf.add_to_collection()", 11 | "tf.AggregationMethod()", 12 | "tf.all_variables()", 13 | "tf.app()", 14 | "tf.app.flags()", 15 | "tf.app.run()", 16 | "tf.arg_max()", 17 | "tf.arg_min()", 18 | "tf.argmax()", 19 | "tf.argmin()", 20 | "tf.as_dtype()", 21 | "tf.as_string()", 22 | "tf.asin()", 23 | "tf.Assert()", 24 | "tf.assert_equal()", 25 | "tf.assert_greater()", 26 | "tf.assert_greater_equal()", 27 | "tf.assert_integer()", 28 | "tf.assert_less()", 29 | "tf.assert_less_equal()", 30 | "tf.assert_negative()", 31 | "tf.assert_non_negative()", 32 | "tf.assert_non_positive()", 33 | "tf.assert_none_equal()", 34 | "tf.assert_positive()", 35 | "tf.assert_proper_iterable()", 36 | "tf.assert_rank()", 37 | "tf.assert_rank_at_least()", 38 | "tf.assert_same_float_dtype()", 39 | "tf.assert_scalar()", 40 | "tf.assert_type()", 41 | "tf.assert_variables_initialized()", 42 | "tf.assign()", 43 | "tf.assign_add()", 44 | "tf.assign_sub()", 45 | "tf.atan()", 46 | "tf.atan2()", 47 | "tf.AttrValue()", 48 | "tf.AttrValue.ListValue()", 49 | "tf.batch_to_space()", 50 | "tf.batch_to_space_nd()", 51 | "tf.betainc()", 52 | "tf.bincount()", 53 | "tf.bitcast()", 54 | "tf.boolean_mask()", 55 | "tf.broadcast_dynamic_shape()", 56 | "tf.broadcast_static_shape()", 57 | "tf.case()", 58 | "tf.cast()", 59 | "tf.ceil()", 60 | "tf.check_numerics()", 61 | "tf.cholesky()", 62 | "tf.cholesky_solve()", 63 | "tf.clip_by_average_norm()", 64 | "tf.clip_by_global_norm()", 65 | "tf.clip_by_norm()", 66 | "tf.clip_by_value()", 67 | "tf.compat()", 68 | "tf.compat.as_bytes()", 69 | "tf.compat.as_str()", 70 | "tf.compat.as_str_any()", 71 | "tf.compat.as_text()", 72 | "tf.complex()", 73 | "tf.concat()", 74 | "tf.cond()", 75 | "tf.ConditionalAccumulator()", 76 | "tf.ConditionalAccumulatorBase()", 77 | "tf.ConfigProto()", 78 | "tf.ConfigProto.DeviceCountEntry()", 79 | "tf.confusion_matrix()", 80 | "tf.conj()", 81 | "tf.constant()", 82 | "tf.constant_initializer()", 83 | "tf.container()", 84 | "tf.contrib()", 85 | "tf.contrib.bayesflow()", 86 | "tf.contrib.bayesflow.entropy()", 87 | "tf.contrib.bayesflow.entropy.elbo_ratio()", 88 | "tf.contrib.bayesflow.entropy.entropy_shannon()", 89 | "tf.contrib.bayesflow.entropy.renyi_alpha()", 90 | "tf.contrib.bayesflow.entropy.renyi_ratio()", 91 | "tf.contrib.bayesflow.monte_carlo()", 92 | "tf.contrib.bayesflow.monte_carlo.expectation()", 93 | "tf.contrib.bayesflow.monte_carlo.expectation_importance_sampler()", 94 | "tf.contrib.bayesflow.monte_carlo.expectation_importance_sampler_logspace()", 95 | "tf.contrib.bayesflow.stochastic_gradient_estimators()", 96 | "tf.contrib.bayesflow.stochastic_graph()", 97 | "tf.contrib.bayesflow.stochastic_graph.surrogate_loss()", 98 | "tf.contrib.bayesflow.stochastic_tensor()", 99 | "tf.contrib.bayesflow.stochastic_tensor.BaseStochasticTensor()", 100 | "tf.contrib.bayesflow.stochastic_tensor.get_current_value_type()", 101 | "tf.contrib.bayesflow.stochastic_tensor.MeanValue()", 102 | "tf.contrib.bayesflow.stochastic_tensor.ObservedStochasticTensor()", 103 | "tf.contrib.bayesflow.stochastic_tensor.SampleValue()", 104 | "tf.contrib.bayesflow.stochastic_tensor.StochasticTensor()", 105 | "tf.contrib.bayesflow.stochastic_tensor.value_type()", 106 | "tf.contrib.bayesflow.stochastic_variables()", 107 | "tf.contrib.bayesflow.variational_inference()", 108 | "tf.contrib.bayesflow.variational_inference.elbo()", 109 | "tf.contrib.bayesflow.variational_inference.elbo_with_log_joint()", 110 | "tf.contrib.bayesflow.variational_inference.ELBOForms()", 111 | "tf.contrib.bayesflow.variational_inference.register_prior()", 112 | "tf.contrib.cloud()", 113 | "tf.contrib.cloud.BigQueryReader()", 114 | "tf.contrib.compiler()", 115 | "tf.contrib.copy_graph()", 116 | "tf.contrib.copy_graph.copy_op_to_graph()", 117 | "tf.contrib.copy_graph.copy_variable_to_graph()", 118 | "tf.contrib.copy_graph.get_copied_op()", 119 | "tf.contrib.crf()", 120 | "tf.contrib.crf.crf_binary_score()", 121 | "tf.contrib.crf.crf_log_likelihood()", 122 | "tf.contrib.crf.crf_log_norm()", 123 | "tf.contrib.crf.crf_sequence_score()", 124 | "tf.contrib.crf.crf_unary_score()", 125 | "tf.contrib.crf.CrfForwardRnnCell()", 126 | "tf.contrib.crf.viterbi_decode()", 127 | "tf.contrib.cudnn_rnn()", 128 | "tf.contrib.cudnn_rnn.CudnnGRU()", 129 | "tf.contrib.cudnn_rnn.CudnnLSTM()", 130 | "tf.contrib.cudnn_rnn.CudnnRNNRelu()", 131 | "tf.contrib.cudnn_rnn.CudnnRNNTanh()", 132 | "tf.contrib.cudnn_rnn.RNNParamsSaveable()", 133 | "tf.contrib.data()", 134 | "tf.contrib.data.Dataset()", 135 | "tf.contrib.data.FixedLengthRecordDataset()", 136 | "tf.contrib.data.Iterator()", 137 | "tf.contrib.data.read_batch_features()", 138 | "tf.contrib.data.rejection_resample()", 139 | "tf.contrib.data.TextLineDataset()", 140 | "tf.contrib.data.TFRecordDataset()", 141 | "tf.contrib.deprecated()", 142 | "tf.contrib.deprecated.audio_summary()", 143 | "tf.contrib.deprecated.histogram_summary()", 144 | "tf.contrib.deprecated.image_summary()", 145 | "tf.contrib.deprecated.merge_all_summaries()", 146 | "tf.contrib.deprecated.merge_summary()", 147 | "tf.contrib.deprecated.scalar_summary()", 148 | "tf.contrib.distributions()", 149 | "tf.contrib.distributions.Bernoulli()", 150 | "tf.contrib.distributions.BernoulliWithSigmoidProbs()", 151 | "tf.contrib.distributions.Beta()", 152 | "tf.contrib.distributions.BetaWithSoftplusConcentration()", 153 | "tf.contrib.distributions.bijectors()", 154 | "tf.contrib.distributions.bijectors.Affine()", 155 | "tf.contrib.distributions.bijectors.AffineLinearOperator()", 156 | "tf.contrib.distributions.bijectors.Bijector()", 157 | "tf.contrib.distributions.bijectors.Chain()", 158 | "tf.contrib.distributions.bijectors.CholeskyOuterProduct()", 159 | "tf.contrib.distributions.bijectors.ConditionalBijector()", 160 | "tf.contrib.distributions.bijectors.Exp()", 161 | "tf.contrib.distributions.bijectors.Identity()", 162 | "tf.contrib.distributions.bijectors.Inline()", 163 | "tf.contrib.distributions.bijectors.Invert()", 164 | "tf.contrib.distributions.bijectors.PowerTransform()", 165 | "tf.contrib.distributions.bijectors.Sigmoid()", 166 | "tf.contrib.distributions.bijectors.SigmoidCentered()", 167 | "tf.contrib.distributions.bijectors.SoftmaxCentered()", 168 | "tf.contrib.distributions.bijectors.Softplus()", 169 | "tf.contrib.distributions.Binomial()", 170 | "tf.contrib.distributions.Categorical()", 171 | "tf.contrib.distributions.Chi2()", 172 | "tf.contrib.distributions.Chi2WithAbsDf()", 173 | "tf.contrib.distributions.ConditionalDistribution()", 174 | "tf.contrib.distributions.ConditionalTransformedDistribution()", 175 | "tf.contrib.distributions.Deterministic()", 176 | "tf.contrib.distributions.Dirichlet()", 177 | "tf.contrib.distributions.DirichletMultinomial()", 178 | "tf.contrib.distributions.Distribution()", 179 | "tf.contrib.distributions.Exponential()", 180 | "tf.contrib.distributions.ExponentialWithSoftplusRate()", 181 | "tf.contrib.distributions.ExpRelaxedOneHotCategorical()", 182 | "tf.contrib.distributions.Gamma()", 183 | "tf.contrib.distributions.GammaWithSoftplusConcentrationRate()", 184 | "tf.contrib.distributions.Geometric()", 185 | "tf.contrib.distributions.InverseGamma()", 186 | "tf.contrib.distributions.InverseGammaWithSoftplusConcentrationRate()", 187 | "tf.contrib.distributions.kl_divergence()", 188 | "tf.contrib.distributions.Laplace()", 189 | "tf.contrib.distributions.LaplaceWithSoftplusScale()", 190 | "tf.contrib.distributions.Logistic()", 191 | "tf.contrib.distributions.matrix_diag_transform()", 192 | "tf.contrib.distributions.Mixture()", 193 | "tf.contrib.distributions.Multinomial()", 194 | "tf.contrib.distributions.MultivariateNormalDiag()", 195 | "tf.contrib.distributions.MultivariateNormalDiagPlusLowRank()", 196 | "tf.contrib.distributions.MultivariateNormalDiagWithSoftplusScale()", 197 | "tf.contrib.distributions.MultivariateNormalFullCovariance()", 198 | "tf.contrib.distributions.MultivariateNormalTriL()", 199 | "tf.contrib.distributions.NegativeBinomial()", 200 | "tf.contrib.distributions.Normal()", 201 | "tf.contrib.distributions.normal_conjugates_known_scale_posterior()", 202 | "tf.contrib.distributions.normal_conjugates_known_scale_predictive()", 203 | "tf.contrib.distributions.NormalWithSoftplusScale()", 204 | "tf.contrib.distributions.OneHotCategorical()", 205 | "tf.contrib.distributions.percentile()", 206 | "tf.contrib.distributions.Poisson()", 207 | "tf.contrib.distributions.QuantizedDistribution()", 208 | "tf.contrib.distributions.RegisterKL()", 209 | "tf.contrib.distributions.RelaxedBernoulli()", 210 | "tf.contrib.distributions.RelaxedOneHotCategorical()", 211 | "tf.contrib.distributions.ReparameterizationType()", 212 | "tf.contrib.distributions.softplus_inverse()", 213 | "tf.contrib.distributions.StudentT()", 214 | "tf.contrib.distributions.StudentTWithAbsDfSoftplusScale()", 215 | "tf.contrib.distributions.TransformedDistribution()", 216 | "tf.contrib.distributions.Uniform()", 217 | "tf.contrib.distributions.VectorDeterministic()", 218 | "tf.contrib.distributions.VectorLaplaceDiag()", 219 | "tf.contrib.distributions.WishartCholesky()", 220 | "tf.contrib.distributions.WishartFull()", 221 | "tf.contrib.factorization()", 222 | "tf.contrib.ffmpeg()", 223 | "tf.contrib.ffmpeg.decode_audio()", 224 | "tf.contrib.ffmpeg.encode_audio()", 225 | "tf.contrib.framework()", 226 | "tf.contrib.framework.add_arg_scope()", 227 | "tf.contrib.framework.add_model_variable()", 228 | "tf.contrib.framework.arg_scope()", 229 | "tf.contrib.framework.arg_scoped_arguments()", 230 | "tf.contrib.framework.assert_global_step()", 231 | "tf.contrib.framework.assert_or_get_global_step()", 232 | "tf.contrib.framework.assert_same_float_dtype()", 233 | "tf.contrib.framework.assert_scalar()", 234 | "tf.contrib.framework.assert_scalar_int()", 235 | "tf.contrib.framework.assign_from_checkpoint()", 236 | "tf.contrib.framework.assign_from_checkpoint_fn()", 237 | "tf.contrib.framework.assign_from_values()", 238 | "tf.contrib.framework.assign_from_values_fn()", 239 | "tf.contrib.framework.convert_to_tensor_or_sparse_tensor()", 240 | "tf.contrib.framework.create_global_step()", 241 | "tf.contrib.framework.deprecated()", 242 | "tf.contrib.framework.deprecated_arg_values()", 243 | "tf.contrib.framework.deprecated_args()", 244 | "tf.contrib.framework.filter_variables()", 245 | "tf.contrib.framework.get_global_step()", 246 | "tf.contrib.framework.get_graph_from_inputs()", 247 | "tf.contrib.framework.get_local_variables()", 248 | "tf.contrib.framework.get_model_variables()", 249 | "tf.contrib.framework.get_name_scope()", 250 | "tf.contrib.framework.get_or_create_global_step()", 251 | "tf.contrib.framework.get_trainable_variables()", 252 | "tf.contrib.framework.get_unique_variable()", 253 | "tf.contrib.framework.get_variable_full_name()", 254 | "tf.contrib.framework.get_variables()", 255 | "tf.contrib.framework.get_variables_by_name()", 256 | "tf.contrib.framework.get_variables_by_suffix()", 257 | "tf.contrib.framework.get_variables_to_restore()", 258 | "tf.contrib.framework.has_arg_scope()", 259 | "tf.contrib.framework.init_from_checkpoint()", 260 | "tf.contrib.framework.is_tensor()", 261 | "tf.contrib.framework.list_variables()", 262 | "tf.contrib.framework.load_checkpoint()", 263 | "tf.contrib.framework.load_variable()", 264 | "tf.contrib.framework.local_variable()", 265 | "tf.contrib.framework.model_variable()", 266 | "tf.contrib.framework.prepend_name_scope()", 267 | "tf.contrib.framework.reduce_sum_n()", 268 | "tf.contrib.framework.remove_squeezable_dimensions()", 269 | "tf.contrib.framework.strip_name_scope()", 270 | "tf.contrib.framework.variable()", 271 | "tf.contrib.framework.VariableDeviceChooser()", 272 | "tf.contrib.framework.with_same_shape()", 273 | "tf.contrib.framework.with_shape()", 274 | "tf.contrib.framework.zero_initializer()", 275 | "tf.contrib.graph_editor()", 276 | "tf.contrib.graph_editor.add_control_inputs()", 277 | "tf.contrib.graph_editor.assign_renamed_collections_handler()", 278 | "tf.contrib.graph_editor.bypass()", 279 | "tf.contrib.graph_editor.can_be_regex()", 280 | "tf.contrib.graph_editor.check_cios()", 281 | "tf.contrib.graph_editor.compute_boundary_ts()", 282 | "tf.contrib.graph_editor.connect()", 283 | "tf.contrib.graph_editor.ControlOutputs()", 284 | "tf.contrib.graph_editor.copy()", 285 | "tf.contrib.graph_editor.copy_op_handler()", 286 | "tf.contrib.graph_editor.copy_with_input_replacements()", 287 | "tf.contrib.graph_editor.detach()", 288 | "tf.contrib.graph_editor.detach_control_inputs()", 289 | "tf.contrib.graph_editor.detach_control_outputs()", 290 | "tf.contrib.graph_editor.detach_inputs()", 291 | "tf.contrib.graph_editor.detach_outputs()", 292 | "tf.contrib.graph_editor.edit()", 293 | "tf.contrib.graph_editor.filter_ops()", 294 | "tf.contrib.graph_editor.filter_ops_from_regex()", 295 | "tf.contrib.graph_editor.filter_ts()", 296 | "tf.contrib.graph_editor.filter_ts_from_regex()", 297 | "tf.contrib.graph_editor.get_backward_walk_ops()", 298 | "tf.contrib.graph_editor.get_consuming_ops()", 299 | "tf.contrib.graph_editor.get_forward_walk_ops()", 300 | "tf.contrib.graph_editor.get_generating_ops()", 301 | "tf.contrib.graph_editor.get_name_scope_ops()", 302 | "tf.contrib.graph_editor.get_ops_ios()", 303 | "tf.contrib.graph_editor.get_tensors()", 304 | "tf.contrib.graph_editor.get_walks_intersection_ops()", 305 | "tf.contrib.graph_editor.get_walks_union_ops()", 306 | "tf.contrib.graph_editor.get_within_boundary_ops()", 307 | "tf.contrib.graph_editor.graph_replace()", 308 | "tf.contrib.graph_editor.keep_t_if_possible_handler()", 309 | "tf.contrib.graph_editor.make_list_of_op()", 310 | "tf.contrib.graph_editor.make_list_of_t()", 311 | "tf.contrib.graph_editor.make_placeholder_from_dtype_and_shape()", 312 | "tf.contrib.graph_editor.make_placeholder_from_tensor()", 313 | "tf.contrib.graph_editor.make_regex()", 314 | "tf.contrib.graph_editor.make_view()", 315 | "tf.contrib.graph_editor.make_view_from_scope()", 316 | "tf.contrib.graph_editor.ph()", 317 | "tf.contrib.graph_editor.placeholder_name()", 318 | "tf.contrib.graph_editor.remove_control_inputs()", 319 | "tf.contrib.graph_editor.replace_t_with_placeholder_handler()", 320 | "tf.contrib.graph_editor.reroute()", 321 | "tf.contrib.graph_editor.reroute_inputs()", 322 | "tf.contrib.graph_editor.reroute_ios()", 323 | "tf.contrib.graph_editor.reroute_outputs()", 324 | "tf.contrib.graph_editor.reroute_ts()", 325 | "tf.contrib.graph_editor.select()", 326 | "tf.contrib.graph_editor.select_ops()", 327 | "tf.contrib.graph_editor.select_ops_and_ts()", 328 | "tf.contrib.graph_editor.select_ts()", 329 | "tf.contrib.graph_editor.sgv()", 330 | "tf.contrib.graph_editor.sgv_scope()", 331 | "tf.contrib.graph_editor.subgraph()", 332 | "tf.contrib.graph_editor.SubGraphView()", 333 | "tf.contrib.graph_editor.swap_inputs()", 334 | "tf.contrib.graph_editor.swap_ios()", 335 | "tf.contrib.graph_editor.swap_outputs()", 336 | "tf.contrib.graph_editor.swap_ts()", 337 | "tf.contrib.graph_editor.transform()", 338 | "tf.contrib.graph_editor.transform_op_if_inside_handler()", 339 | "tf.contrib.graph_editor.Transformer()", 340 | "tf.contrib.graph_editor.TransformerInfo()", 341 | "tf.contrib.graph_editor.util()", 342 | "tf.contrib.grid_rnn()", 343 | "tf.contrib.image()", 344 | "tf.contrib.image.angles_to_projective_transforms()", 345 | "tf.contrib.image.compose_transforms()", 346 | "tf.contrib.image.rotate()", 347 | "tf.contrib.image.single_image_random_dot_stereograms()", 348 | "tf.contrib.image.transform()", 349 | "tf.contrib.input_pipeline()", 350 | "tf.contrib.input_pipeline.obtain_next()", 351 | "tf.contrib.integrate()", 352 | "tf.contrib.integrate.odeint()", 353 | "tf.contrib.keras()", 354 | "tf.contrib.keras.activations()", 355 | "tf.contrib.keras.activations.deserialize()", 356 | "tf.contrib.keras.activations.elu()", 357 | "tf.contrib.keras.activations.get()", 358 | "tf.contrib.keras.activations.hard_sigmoid()", 359 | "tf.contrib.keras.activations.linear()", 360 | "tf.contrib.keras.activations.relu()", 361 | "tf.contrib.keras.activations.serialize()", 362 | "tf.contrib.keras.activations.sigmoid()", 363 | "tf.contrib.keras.activations.softmax()", 364 | "tf.contrib.keras.activations.softplus()", 365 | "tf.contrib.keras.activations.softsign()", 366 | "tf.contrib.keras.activations.tanh()", 367 | "tf.contrib.keras.api()", 368 | "tf.contrib.keras.applications()", 369 | "tf.contrib.keras.applications.inception_v3()", 370 | "tf.contrib.keras.applications.inception_v3.decode_predictions()", 371 | "tf.contrib.keras.applications.inception_v3.InceptionV3()", 372 | "tf.contrib.keras.applications.inception_v3.preprocess_input()", 373 | "tf.contrib.keras.applications.InceptionV3()", 374 | "tf.contrib.keras.applications.ResNet50()", 375 | "tf.contrib.keras.applications.resnet50()", 376 | "tf.contrib.keras.applications.resnet50.decode_predictions()", 377 | "tf.contrib.keras.applications.resnet50.preprocess_input()", 378 | 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| "tf.eye()", 1254 | "tf.fake_quant_with_min_max_args()", 1255 | "tf.fake_quant_with_min_max_args_gradient()", 1256 | "tf.fake_quant_with_min_max_vars()", 1257 | "tf.fake_quant_with_min_max_vars_gradient()", 1258 | "tf.fake_quant_with_min_max_vars_per_channel()", 1259 | "tf.fake_quant_with_min_max_vars_per_channel_gradient()", 1260 | "tf.feature_column()", 1261 | "tf.feature_column.bucketized_column()", 1262 | "tf.feature_column.categorical_column_with_hash_bucket()", 1263 | "tf.feature_column.categorical_column_with_identity()", 1264 | "tf.feature_column.categorical_column_with_vocabulary_file()", 1265 | "tf.feature_column.categorical_column_with_vocabulary_list()", 1266 | "tf.feature_column.crossed_column()", 1267 | "tf.feature_column.embedding_column()", 1268 | "tf.feature_column.indicator_column()", 1269 | "tf.feature_column.input_layer()", 1270 | "tf.feature_column.linear_model()", 1271 | "tf.feature_column.make_parse_example_spec()", 1272 | "tf.feature_column.numeric_column()", 1273 | "tf.feature_column.weighted_categorical_column()", 1274 | "tf.fft()", 1275 | "tf.fft2d()", 1276 | "tf.fft3d()", 1277 | "tf.FIFOQueue()", 1278 | "tf.fill()", 1279 | "tf.fixed_size_partitioner()", 1280 | "tf.FixedLenFeature()", 1281 | "tf.FixedLengthRecordReader()", 1282 | "tf.FixedLenSequenceFeature()", 1283 | "tf.flags()", 1284 | "tf.floor()", 1285 | "tf.floor_div()", 1286 | "tf.floordiv()", 1287 | "tf.floormod()", 1288 | "tf.foldl()", 1289 | "tf.foldr()", 1290 | "tf.gather()", 1291 | "tf.gather_nd()", 1292 | "tf.get_collection()", 1293 | "tf.get_collection_ref()", 1294 | "tf.get_default_graph()", 1295 | "tf.get_default_session()", 1296 | "tf.get_local_variable()", 1297 | "tf.get_seed()", 1298 | "tf.get_session_handle()", 1299 | "tf.get_session_tensor()", 1300 | "tf.get_variable()", 1301 | "tf.get_variable_scope()", 1302 | "tf.gfile()", 1303 | "tf.gfile.Copy()", 1304 | "tf.gfile.DeleteRecursively()", 1305 | "tf.gfile.Exists()", 1306 | "tf.gfile.FastGFile()", 1307 | 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| "tf.identity()", 1340 | "tf.IdentityReader()", 1341 | "tf.ifft()", 1342 | "tf.ifft2d()", 1343 | "tf.ifft3d()", 1344 | "tf.igamma()", 1345 | "tf.igammac()", 1346 | "tf.imag()", 1347 | "tf.image()", 1348 | "tf.image.adjust_brightness()", 1349 | "tf.image.adjust_contrast()", 1350 | "tf.image.adjust_gamma()", 1351 | "tf.image.adjust_hue()", 1352 | "tf.image.adjust_saturation()", 1353 | "tf.image.central_crop()", 1354 | "tf.image.convert_image_dtype()", 1355 | "tf.image.crop_and_resize()", 1356 | "tf.image.crop_to_bounding_box()", 1357 | "tf.image.decode_gif()", 1358 | "tf.image.decode_image()", 1359 | "tf.image.decode_jpeg()", 1360 | "tf.image.decode_png()", 1361 | "tf.image.draw_bounding_boxes()", 1362 | "tf.image.encode_jpeg()", 1363 | "tf.image.encode_png()", 1364 | "tf.image.extract_glimpse()", 1365 | "tf.image.flip_left_right()", 1366 | "tf.image.flip_up_down()", 1367 | "tf.image.grayscale_to_rgb()", 1368 | "tf.image.hsv_to_rgb()", 1369 | "tf.image.non_max_suppression()", 1370 | 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1428 | "tf.load_file_system_library()", 1429 | "tf.load_op_library()", 1430 | "tf.local_variables()", 1431 | "tf.local_variables_initializer()", 1432 | "tf.log()", 1433 | "tf.log1p()", 1434 | "tf.log_sigmoid()", 1435 | "tf.logging()", 1436 | "tf.logging.debug()", 1437 | "tf.logging.error()", 1438 | "tf.logging.fatal()", 1439 | "tf.logging.flush()", 1440 | "tf.logging.get_verbosity()", 1441 | "tf.logging.info()", 1442 | "tf.logging.log()", 1443 | "tf.logging.log_every_n()", 1444 | "tf.logging.log_first_n()", 1445 | "tf.logging.log_if()", 1446 | "tf.logging.set_verbosity()", 1447 | "tf.logging.TaskLevelStatusMessage()", 1448 | "tf.logging.vlog()", 1449 | "tf.logging.warn()", 1450 | "tf.logging.warning()", 1451 | "tf.logical_and()", 1452 | "tf.logical_not()", 1453 | "tf.logical_or()", 1454 | "tf.logical_xor()", 1455 | "tf.LogMessage()", 1456 | "tf.losses()", 1457 | "tf.losses.absolute_difference()", 1458 | "tf.losses.add_loss()", 1459 | "tf.losses.compute_weighted_loss()", 1460 | 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| "tf.matrix_triangular_solve()", 1490 | "tf.maximum()", 1491 | "tf.meshgrid()", 1492 | "tf.MetaGraphDef()", 1493 | "tf.MetaGraphDef.CollectionDefEntry()", 1494 | "tf.MetaGraphDef.MetaInfoDef()", 1495 | "tf.MetaGraphDef.SignatureDefEntry()", 1496 | "tf.metrics()", 1497 | "tf.metrics.accuracy()", 1498 | "tf.metrics.auc()", 1499 | "tf.metrics.false_negatives()", 1500 | "tf.metrics.false_positives()", 1501 | "tf.metrics.mean()", 1502 | "tf.metrics.mean_absolute_error()", 1503 | "tf.metrics.mean_cosine_distance()", 1504 | "tf.metrics.mean_iou()", 1505 | "tf.metrics.mean_per_class_accuracy()", 1506 | "tf.metrics.mean_relative_error()", 1507 | "tf.metrics.mean_squared_error()", 1508 | "tf.metrics.mean_tensor()", 1509 | "tf.metrics.percentage_below()", 1510 | "tf.metrics.precision()", 1511 | "tf.metrics.precision_at_thresholds()", 1512 | "tf.metrics.recall()", 1513 | "tf.metrics.recall_at_k()", 1514 | "tf.metrics.recall_at_thresholds()", 1515 | "tf.metrics.root_mean_squared_error()", 1516 | 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1545 | "tf.nn.conv2d_backprop_filter()", 1546 | "tf.nn.conv2d_backprop_input()", 1547 | "tf.nn.conv2d_transpose()", 1548 | "tf.nn.conv3d()", 1549 | "tf.nn.conv3d_backprop_filter_v2()", 1550 | "tf.nn.conv3d_transpose()", 1551 | "tf.nn.convolution()", 1552 | "tf.nn.crelu()", 1553 | "tf.nn.ctc_beam_search_decoder()", 1554 | "tf.nn.ctc_greedy_decoder()", 1555 | "tf.nn.ctc_loss()", 1556 | "tf.nn.depthwise_conv2d()", 1557 | "tf.nn.depthwise_conv2d_native()", 1558 | "tf.nn.depthwise_conv2d_native_backprop_filter()", 1559 | "tf.nn.depthwise_conv2d_native_backprop_input()", 1560 | "tf.nn.dilation2d()", 1561 | "tf.nn.dropout()", 1562 | "tf.nn.dynamic_rnn()", 1563 | "tf.nn.elu()", 1564 | "tf.nn.embedding_lookup()", 1565 | "tf.nn.embedding_lookup_sparse()", 1566 | "tf.nn.erosion2d()", 1567 | "tf.nn.fixed_unigram_candidate_sampler()", 1568 | "tf.nn.fractional_avg_pool()", 1569 | "tf.nn.fractional_max_pool()", 1570 | "tf.nn.fused_batch_norm()", 1571 | "tf.nn.in_top_k()", 1572 | "tf.nn.l2_loss()", 1573 | "tf.nn.l2_normalize()", 1574 | "tf.nn.learned_unigram_candidate_sampler()", 1575 | "tf.nn.local_response_normalization()", 1576 | "tf.nn.log_poisson_loss()", 1577 | "tf.nn.log_softmax()", 1578 | "tf.nn.log_uniform_candidate_sampler()", 1579 | "tf.nn.lrn()", 1580 | "tf.nn.max_pool()", 1581 | "tf.nn.max_pool3d()", 1582 | "tf.nn.max_pool_with_argmax()", 1583 | "tf.nn.moments()", 1584 | "tf.nn.nce_loss()", 1585 | "tf.nn.normalize_moments()", 1586 | "tf.nn.pool()", 1587 | "tf.nn.quantized_avg_pool()", 1588 | "tf.nn.quantized_conv2d()", 1589 | "tf.nn.quantized_max_pool()", 1590 | "tf.nn.quantized_relu_x()", 1591 | "tf.nn.raw_rnn()", 1592 | "tf.nn.relu()", 1593 | "tf.nn.relu6()", 1594 | "tf.nn.relu_layer()", 1595 | "tf.nn.rnn_cell()", 1596 | "tf.nn.rnn_cell.BasicLSTMCell()", 1597 | "tf.nn.rnn_cell.BasicRNNCell()", 1598 | "tf.nn.rnn_cell.DeviceWrapper()", 1599 | "tf.nn.rnn_cell.DropoutWrapper()", 1600 | "tf.nn.rnn_cell.GRUCell()", 1601 | "tf.nn.rnn_cell.LSTMCell()", 1602 | "tf.nn.rnn_cell.LSTMStateTuple()", 1603 | "tf.nn.rnn_cell.MultiRNNCell()", 1604 | "tf.nn.rnn_cell.ResidualWrapper()", 1605 | "tf.nn.rnn_cell.RNNCell()", 1606 | "tf.nn.sampled_softmax_loss()", 1607 | "tf.nn.separable_conv2d()", 1608 | "tf.nn.sigmoid()", 1609 | "tf.nn.sigmoid_cross_entropy_with_logits()", 1610 | "tf.nn.softmax()", 1611 | "tf.nn.softmax_cross_entropy_with_logits()", 1612 | "tf.nn.softplus()", 1613 | "tf.nn.softsign()", 1614 | "tf.nn.sparse_softmax_cross_entropy_with_logits()", 1615 | "tf.nn.static_bidirectional_rnn()", 1616 | "tf.nn.static_rnn()", 1617 | "tf.nn.static_state_saving_rnn()", 1618 | "tf.nn.sufficient_statistics()", 1619 | "tf.nn.tanh()", 1620 | "tf.nn.top_k()", 1621 | "tf.nn.uniform_candidate_sampler()", 1622 | "tf.nn.weighted_cross_entropy_with_logits()", 1623 | "tf.nn.weighted_moments()", 1624 | "tf.nn.with_space_to_batch()", 1625 | "tf.nn.xw_plus_b()", 1626 | "tf.nn.zero_fraction()", 1627 | "tf.no_op()", 1628 | "tf.no_regularizer()", 1629 | "tf.NodeDef()", 1630 | "tf.NodeDef.AttrEntry()", 1631 | "tf.NoGradient()", 1632 | "tf.norm()", 1633 | "tf.not_equal()", 1634 | "tf.NotDifferentiable()", 1635 | "tf.one_hot()", 1636 | "tf.ones()", 1637 | "tf.ones_initializer()", 1638 | "tf.ones_like()", 1639 | "tf.op_scope()", 1640 | "tf.Operation()", 1641 | "tf.OpError()", 1642 | "tf.OptimizerOptions()", 1643 | "tf.orthogonal_initializer()", 1644 | "tf.pad()", 1645 | "tf.PaddingFIFOQueue()", 1646 | "tf.parallel_stack()", 1647 | "tf.parse_example()", 1648 | "tf.parse_single_example()", 1649 | "tf.parse_single_sequence_example()", 1650 | "tf.parse_tensor()", 1651 | "tf.placeholder()", 1652 | "tf.placeholder_with_default()", 1653 | "tf.polygamma()", 1654 | "tf.pow()", 1655 | "tf.Print()", 1656 | "tf.PriorityQueue()", 1657 | "tf.py_func()", 1658 | "tf.python_io()", 1659 | "tf.python_io.tf_record_iterator()", 1660 | "tf.python_io.TFRecordCompressionType()", 1661 | "tf.python_io.TFRecordOptions()", 1662 | "tf.python_io.TFRecordWriter()", 1663 | 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1697 | "tf.reset_default_graph()", 1698 | "tf.reshape()", 1699 | "tf.resource_loader()", 1700 | "tf.resource_loader.get_data_files_path()", 1701 | "tf.resource_loader.get_path_to_datafile()", 1702 | "tf.resource_loader.get_root_dir_with_all_resources()", 1703 | "tf.resource_loader.load_resource()", 1704 | "tf.resource_loader.readahead_file_path()", 1705 | "tf.reverse()", 1706 | "tf.reverse_sequence()", 1707 | "tf.reverse_v2()", 1708 | "tf.rint()", 1709 | "tf.round()", 1710 | "tf.rsqrt()", 1711 | "tf.RunMetadata()", 1712 | "tf.RunOptions()", 1713 | "tf.saturate_cast()", 1714 | "tf.saved_model()", 1715 | "tf.saved_model.builder()", 1716 | "tf.saved_model.builder.SavedModelBuilder()", 1717 | "tf.saved_model.constants()", 1718 | "tf.saved_model.loader()", 1719 | "tf.saved_model.loader.load()", 1720 | "tf.saved_model.loader.maybe_saved_model_directory()", 1721 | "tf.saved_model.main_op()", 1722 | "tf.saved_model.main_op.main_op()", 1723 | "tf.saved_model.main_op.main_op_with_restore()", 1724 | "tf.saved_model.signature_constants()", 1725 | "tf.saved_model.signature_def_utils()", 1726 | "tf.saved_model.signature_def_utils.build_signature_def()", 1727 | "tf.saved_model.signature_def_utils.classification_signature_def()", 1728 | "tf.saved_model.signature_def_utils.predict_signature_def()", 1729 | "tf.saved_model.signature_def_utils.regression_signature_def()", 1730 | "tf.saved_model.tag_constants()", 1731 | "tf.saved_model.utils()", 1732 | "tf.saved_model.utils.build_tensor_info()", 1733 | "tf.scalar_mul()", 1734 | "tf.scan()", 1735 | "tf.scatter_add()", 1736 | "tf.scatter_div()", 1737 | "tf.scatter_mul()", 1738 | "tf.scatter_nd()", 1739 | "tf.scatter_nd_add()", 1740 | "tf.scatter_nd_sub()", 1741 | "tf.scatter_nd_update()", 1742 | "tf.scatter_sub()", 1743 | "tf.scatter_update()", 1744 | "tf.segment_max()", 1745 | "tf.segment_mean()", 1746 | "tf.segment_min()", 1747 | "tf.segment_prod()", 1748 | "tf.segment_sum()", 1749 | "tf.self_adjoint_eig()", 1750 | "tf.self_adjoint_eigvals()", 1751 | "tf.sequence_mask()", 1752 | "tf.serialize_many_sparse()", 1753 | "tf.serialize_sparse()", 1754 | "tf.Session()", 1755 | "tf.SessionLog()", 1756 | "tf.set_random_seed()", 1757 | "tf.setdiff1d()", 1758 | "tf.sets()", 1759 | "tf.sets.set_difference()", 1760 | "tf.sets.set_intersection()", 1761 | "tf.sets.set_size()", 1762 | "tf.sets.set_union()", 1763 | "tf.shape()", 1764 | "tf.shape_n()", 1765 | "tf.sigmoid()", 1766 | "tf.sign()", 1767 | "tf.sin()", 1768 | "tf.size()", 1769 | "tf.slice()", 1770 | "tf.space_to_batch()", 1771 | "tf.space_to_batch_nd()", 1772 | "tf.space_to_depth()", 1773 | "tf.sparse_add()", 1774 | "tf.sparse_concat()", 1775 | "tf.sparse_fill_empty_rows()", 1776 | "tf.sparse_mask()", 1777 | "tf.sparse_matmul()", 1778 | "tf.sparse_maximum()", 1779 | "tf.sparse_merge()", 1780 | "tf.sparse_minimum()", 1781 | "tf.sparse_placeholder()", 1782 | "tf.sparse_reduce_sum()", 1783 | "tf.sparse_reduce_sum_sparse()", 1784 | "tf.sparse_reorder()", 1785 | "tf.sparse_reset_shape()", 1786 | "tf.sparse_reshape()", 1787 | "tf.sparse_retain()", 1788 | "tf.sparse_segment_mean()", 1789 | "tf.sparse_segment_sqrt_n()", 1790 | "tf.sparse_segment_sum()", 1791 | "tf.sparse_softmax()", 1792 | "tf.sparse_split()", 1793 | "tf.sparse_tensor_dense_matmul()", 1794 | "tf.sparse_tensor_to_dense()", 1795 | "tf.sparse_to_dense()", 1796 | "tf.sparse_to_indicator()", 1797 | "tf.sparse_transpose()", 1798 | "tf.SparseConditionalAccumulator()", 1799 | "tf.SparseFeature()", 1800 | "tf.SparseTensor()", 1801 | "tf.SparseTensorValue()", 1802 | "tf.spectral()", 1803 | "tf.spectral.fft()", 1804 | "tf.spectral.fft2d()", 1805 | "tf.spectral.fft3d()", 1806 | "tf.spectral.ifft()", 1807 | "tf.spectral.ifft2d()", 1808 | "tf.spectral.ifft3d()", 1809 | "tf.spectral.irfft()", 1810 | "tf.spectral.irfft2d()", 1811 | "tf.spectral.irfft3d()", 1812 | "tf.spectral.rfft()", 1813 | "tf.spectral.rfft2d()", 1814 | "tf.spectral.rfft3d()", 1815 | "tf.split()", 1816 | "tf.sqrt()", 1817 | "tf.square()", 1818 | "tf.squared_difference()", 1819 | "tf.squeeze()", 1820 | "tf.stack()", 1821 | "tf.stop_gradient()", 1822 | "tf.strided_slice()", 1823 | "tf.string_join()", 1824 | "tf.string_split()", 1825 | "tf.string_to_hash_bucket()", 1826 | "tf.string_to_hash_bucket_fast()", 1827 | "tf.string_to_hash_bucket_strong()", 1828 | "tf.string_to_number()", 1829 | "tf.substr()", 1830 | "tf.subtract()", 1831 | "tf.Summary()", 1832 | "tf.summary()", 1833 | "tf.Summary.Audio()", 1834 | "tf.summary.audio()", 1835 | "tf.summary.Event()", 1836 | "tf.summary.FileWriter()", 1837 | "tf.summary.FileWriterCache()", 1838 | "tf.summary.get_summary_description()", 1839 | "tf.summary.histogram()", 1840 | "tf.Summary.Image()", 1841 | "tf.summary.image()", 1842 | "tf.summary.merge()", 1843 | "tf.summary.merge_all()", 1844 | "tf.summary.scalar()", 1845 | "tf.summary.SessionLog()", 1846 | "tf.summary.Summary()", 1847 | "tf.summary.Summary.Audio()", 1848 | "tf.summary.Summary.Image()", 1849 | "tf.summary.Summary.Value()", 1850 | "tf.summary.SummaryDescription()", 1851 | "tf.summary.TaggedRunMetadata()", 1852 | "tf.summary.tensor_summary()", 1853 | "tf.summary.text()", 1854 | "tf.Summary.Value()", 1855 | "tf.svd()", 1856 | "tf.sysconfig()", 1857 | "tf.sysconfig.get_include()", 1858 | "tf.sysconfig.get_lib()", 1859 | "tf.tables_initializer()", 1860 | "tf.tan()", 1861 | "tf.tanh()", 1862 | "tf.Tensor()", 1863 | "tf.TensorArray()", 1864 | "tf.tensordot()", 1865 | "tf.TensorInfo()", 1866 | "tf.TensorShape()", 1867 | "tf.test()", 1868 | "tf.test.assert_equal_graph_def()", 1869 | "tf.test.Benchmark()", 1870 | "tf.test.compute_gradient()", 1871 | "tf.test.compute_gradient_error()", 1872 | "tf.test.create_local_cluster()", 1873 | "tf.test.get_temp_dir()", 1874 | "tf.test.gpu_device_name()", 1875 | "tf.test.is_built_with_cuda()", 1876 | "tf.test.is_gpu_available()", 1877 | "tf.test.main()", 1878 | "tf.test.mock()", 1879 | "tf.test.StubOutForTesting()", 1880 | "tf.test.test_src_dir_path()", 1881 | "tf.test.TestCase()", 1882 | "tf.test.TestCase.failureException()", 1883 | "tf.TextLineReader()", 1884 | "tf.TFRecordReader()", 1885 | "tf.tile()", 1886 | "tf.to_bfloat16()", 1887 | "tf.to_double()", 1888 | "tf.to_float()", 1889 | "tf.to_int32()", 1890 | "tf.to_int64()", 1891 | "tf.tools()", 1892 | "tf.trace()", 1893 | "tf.train()", 1894 | "tf.train.AdadeltaOptimizer()", 1895 | "tf.train.AdagradDAOptimizer()", 1896 | "tf.train.AdagradOptimizer()", 1897 | "tf.train.AdamOptimizer()", 1898 | "tf.train.add_queue_runner()", 1899 | "tf.train.assert_global_step()", 1900 | "tf.train.basic_train_loop()", 1901 | "tf.train.batch()", 1902 | "tf.train.batch_join()", 1903 | "tf.train.BytesList()", 1904 | "tf.train.checkpoint_exists()", 1905 | "tf.train.CheckpointSaverHook()", 1906 | "tf.train.CheckpointSaverListener()", 1907 | "tf.train.ChiefSessionCreator()", 1908 | "tf.train.ClusterDef()", 1909 | "tf.train.ClusterSpec()", 1910 | "tf.train.Coordinator()", 1911 | "tf.train.create_global_step()", 1912 | "tf.train.do_quantize_training_on_graphdef()", 1913 | "tf.train.Example()", 1914 | "tf.train.exponential_decay()", 1915 | "tf.train.ExponentialMovingAverage()", 1916 | "tf.train.export_meta_graph()", 1917 | "tf.train.Feature()", 1918 | "tf.train.FeatureList()", 1919 | "tf.train.FeatureLists()", 1920 | "tf.train.FeatureLists.FeatureListEntry()", 1921 | "tf.train.Features()", 1922 | "tf.train.Features.FeatureEntry()", 1923 | "tf.train.FeedFnHook()", 1924 | "tf.train.FinalOpsHook()", 1925 | "tf.train.FloatList()", 1926 | "tf.train.FtrlOptimizer()", 1927 | "tf.train.generate_checkpoint_state_proto()", 1928 | "tf.train.get_checkpoint_mtimes()", 1929 | "tf.train.get_checkpoint_state()", 1930 | "tf.train.get_global_step()", 1931 | "tf.train.get_or_create_global_step()", 1932 | "tf.train.global_step()", 1933 | "tf.train.GlobalStepWaiterHook()", 1934 | "tf.train.GradientDescentOptimizer()", 1935 | "tf.train.import_meta_graph()", 1936 | "tf.train.input_producer()", 1937 | "tf.train.Int64List()", 1938 | "tf.train.inverse_time_decay()", 1939 | "tf.train.JobDef()", 1940 | "tf.train.JobDef.TasksEntry()", 1941 | "tf.train.latest_checkpoint()", 1942 | "tf.train.limit_epochs()", 1943 | "tf.train.LoggingTensorHook()", 1944 | "tf.train.LooperThread()", 1945 | "tf.train.match_filenames_once()", 1946 | "tf.train.maybe_batch()", 1947 | "tf.train.maybe_batch_join()", 1948 | "tf.train.maybe_shuffle_batch()", 1949 | "tf.train.maybe_shuffle_batch_join()", 1950 | "tf.train.MomentumOptimizer()", 1951 | "tf.train.MonitoredSession()", 1952 | "tf.train.MonitoredTrainingSession()", 1953 | "tf.train.NanLossDuringTrainingError()", 1954 | "tf.train.NanTensorHook()", 1955 | "tf.train.natural_exp_decay()", 1956 | "tf.train.NewCheckpointReader()", 1957 | "tf.train.Optimizer()", 1958 | "tf.train.piecewise_constant()", 1959 | "tf.train.polynomial_decay()", 1960 | "tf.train.ProximalAdagradOptimizer()", 1961 | "tf.train.ProximalGradientDescentOptimizer()", 1962 | "tf.train.queue_runner()", 1963 | "tf.train.queue_runner.add_queue_runner()", 1964 | "tf.train.queue_runner.QueueRunner()", 1965 | "tf.train.queue_runner.start_queue_runners()", 1966 | "tf.train.QueueRunner()", 1967 | "tf.train.range_input_producer()", 1968 | "tf.train.replica_device_setter()", 1969 | "tf.train.RMSPropOptimizer()", 1970 | "tf.train.Saver()", 1971 | "tf.train.SaverDef()", 1972 | "tf.train.Scaffold()", 1973 | "tf.train.sdca_fprint()", 1974 | "tf.train.sdca_optimizer()", 1975 | "tf.train.sdca_shrink_l1()", 1976 | "tf.train.SecondOrStepTimer()", 1977 | "tf.train.SequenceExample()", 1978 | "tf.train.Server()", 1979 | "tf.train.ServerDef()", 1980 | "tf.train.SessionCreator()", 1981 | "tf.train.SessionManager()", 1982 | "tf.train.SessionRunArgs()", 1983 | "tf.train.SessionRunContext()", 1984 | "tf.train.SessionRunHook()", 1985 | "tf.train.SessionRunValues()", 1986 | "tf.train.shuffle_batch()", 1987 | "tf.train.shuffle_batch_join()", 1988 | "tf.train.SingularMonitoredSession()", 1989 | "tf.train.slice_input_producer()", 1990 | "tf.train.start_queue_runners()", 1991 | "tf.train.StepCounterHook()", 1992 | "tf.train.StopAtStepHook()", 1993 | "tf.train.string_input_producer()", 1994 | "tf.train.summary_iterator()", 1995 | "tf.train.SummarySaverHook()", 1996 | "tf.train.Supervisor()", 1997 | "tf.train.SyncReplicasOptimizer()", 1998 | "tf.train.update_checkpoint_state()", 1999 | "tf.train.WorkerSessionCreator()", 2000 | "tf.train.write_graph()", 2001 | "tf.trainable_variables()", 2002 | "tf.transpose()", 2003 | "tf.truediv()", 2004 | "tf.truncated_normal()", 2005 | "tf.truncated_normal_initializer()", 2006 | "tf.truncatediv()", 2007 | "tf.truncatemod()", 2008 | "tf.tuple()", 2009 | "tf.uniform_unit_scaling_initializer()", 2010 | "tf.unique()", 2011 | "tf.unique_with_counts()", 2012 | "tf.unsorted_segment_max()", 2013 | "tf.unsorted_segment_sum()", 2014 | "tf.unstack()", 2015 | "tf.user_ops()", 2016 | "tf.Variable()", 2017 | "tf.Variable.SaveSliceInfo()", 2018 | "tf.variable_axis_size_partitioner()", 2019 | "tf.variable_op_scope()", 2020 | "tf.variable_scope()", 2021 | "tf.variables_initializer()", 2022 | "tf.VariableScope()", 2023 | "tf.VarLenFeature()", 2024 | "tf.verify_tensor_all_finite()", 2025 | "tf.where()", 2026 | "tf.while_loop()", 2027 | "tf.WholeFileReader()", 2028 | "tf.write_file()", 2029 | "tf.zeros()", 2030 | "tf.zeros_initializer()", 2031 | "tf.zeros_like()", 2032 | "tf.zeta()", 2033 | "tfdbg()", 2034 | "tfdbg.add_debug_tensor_watch()", 2035 | "tfdbg.DebugDumpDir()", 2036 | "tfdbg.DebugTensorDatum()", 2037 | "tfdbg.DumpingDebugHook()", 2038 | "tfdbg.DumpingDebugWrapperSession()", 2039 | "tfdbg.GrpcDebugHook()", 2040 | "tfdbg.GrpcDebugWrapperSession()", 2041 | "tfdbg.has_inf_or_nan()", 2042 | "tfdbg.load_tensor_from_event_file()", 2043 | "tfdbg.LocalCLIDebugHook()", 2044 | "tfdbg.LocalCLIDebugWrapperSession()", 2045 | "tfdbg.watch_graph()", 2046 | "tfdbg.watch_graph_with_blacklists", 2047 | "tfdbg.WatchOptions()", 2048 | ] 2049 | -------------------------------------------------------------------------------- /message.json: -------------------------------------------------------------------------------- 1 | { 2 | "install": "README.md" 3 | } -------------------------------------------------------------------------------- /sublime_tensorflow_autocomplete.py: -------------------------------------------------------------------------------- 1 | import re 2 | import sublime 3 | import sublime_plugin 4 | import webbrowser 5 | from .helper import get_tf_functions 6 | 7 | RE_TRIGGER_BEFORE = re.compile( 8 | r"\w*(\.[\w\.]+)" 9 | ) 10 | 11 | tf_functions = get_tf_functions() 12 | 13 | 14 | class TensorflowAutocomplete(sublime_plugin.EventListener): 15 | 16 | def __init__(self): 17 | 18 | self.tf_completions = [ 19 | ("%s \tTensorflow" % s, s.replace("()", "($1)")) 20 | for s in tf_functions 21 | ] 22 | 23 | def on_query_completions(self, view, prefix, locations): 24 | 25 | loc = locations[0] 26 | if not view.match_selector(loc, 'source.python'): 27 | return 28 | 29 | completions = self.tf_completions 30 | # get the inverted line before the location 31 | line_before_reg = sublime.Region(view.line(loc).a, loc) 32 | line_before = view.substr(line_before_reg)[::-1] 33 | 34 | # check if it matches the trigger 35 | m = RE_TRIGGER_BEFORE.match(line_before) 36 | if m: 37 | # get the text before the . 38 | trigger = m.group(1)[::-1] 39 | # filter and strip the completions 40 | completions = [ 41 | (c[0], c[1][len(trigger):]) 42 | for c in completions 43 | if c[1].startswith(trigger) 44 | ] 45 | 46 | return completions 47 | 48 | 49 | class TensorflowDocCommand(sublime_plugin.TextCommand): 50 | 51 | def run(self, edit): 52 | 53 | selection = "" 54 | regions = self.view.sel() 55 | 56 | # if there is only one cursor and the cursor selects nothing, 57 | # we try to find a tf function on the cursor position. 58 | if len(regions) == 1 and len(regions[0]) == 0: 59 | selection = self.view.substr( 60 | self.extend_point_by_allowed_chars( 61 | point=regions[0].a, 62 | allowed_chars='0123456789abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ._', 63 | ) 64 | ) 65 | else: 66 | for region in self.view.sel(): 67 | selection += self.view.substr(region) 68 | 69 | if selection + "()" in tf_functions: 70 | selec_link = selection.replace('.', '/') 71 | webbrowser.open("https://www.tensorflow.org/api_docs/python/%s" % selec_link) 72 | else: 73 | sublime.error_message( 74 | "'%s' is not a Tensorflow class or function.\n" 75 | "Here is an example of what can be selected: 'tf.nn.conv2d'" 76 | % (selection) 77 | ) 78 | 79 | def extend_point_by_allowed_chars(self, point, allowed_chars): 80 | bound_l = bound_r = point 81 | # extend left 82 | while self.view.substr(bound_l) in allowed_chars: 83 | bound_l -= 1 84 | # extend right 85 | while self.view.substr(bound_r) in allowed_chars: 86 | bound_r += 1 87 | return sublime.Region(bound_l+1, bound_r) 88 | --------------------------------------------------------------------------------