Tag: loss=’sparse_categorical_crossentropy’

Multiple output values on tensorflow [closed]

Post author By Full Stack
Post date March 15, 2020
No Comments on Multiple output values on tensorflow [closed]

I want to make a tensorflow model that would return me 14400 values. I have input of shape (1, 7854) and output of shape (1, 14400). Using this data I get error: ValueError: Can not squeeze dim[1], expected a dimension of 1, got 14400 for ‘metrics/accuracy/Squeeze’ (op: ‘Squeeze’) with input shapes: [?,14400]. My code is: […]

Tags )), 1) and it worked, 14400) model.fit(train_data, 14400)?, 14400). Using this data I get error: ValueError: Can not squeeze dim[1], 14400]. My code is: model = tf.keras.models.Sequential([ tf.keras.layers.Dense(10000, 7854) and output of shape (1, 7854) output = ... # numpy array of shape (1, activation=tf.nn.relu), activation=tf.nn.relu) ]) model.compile(optimizer='adam', epochs=10) I've tested this code on output shape of (1, expected a dimension of 1, got 14400 for 'metrics/accuracy/Squeeze' (op: 'Squeeze') with input shapes: [?, I want to make a tensorflow model that would return me 14400 values. I have input of shape (1, input_shape=(7854, loss='sparse_categorical_crossentropy', metrics=['accuracy']) train_data = ... # numpy array of shape (1, Output, so my question is how to make the model work on output of shape (1, tf.keras.layers.Dense(10000

Artificial Intelligence (AI) Development

Trouble with EarlyStopping in tf.keras

Post author By Jessica Alba
Post date March 11, 2020
No Comments on Trouble with EarlyStopping in tf.keras

I am training my first transferred learning model(yay!) and I am having trouble getting the model to stop training when the validation loss hasn’t changed by more than 0.1 in more than 3 epochs. Here is the relevant block of code early_stopping = tf.keras.callbacks.EarlyStopping(monitor=’val_loss’, patience=3, min_delta = 0.1) model.compile(optimizer=’adam’, loss=’sparse_categorical_crossentropy’, metrics=[‘accuracy’], callbacks=[early_stopping]) EPOCHS = 100 […]

Tags callbacks=[early_stopping]) EPOCHS = 100 history = model.fit(training_batches, can I get a link to a tutorial?, epochs=epochs, I am training my first transferred learning model(yay!) and I am having trouble getting the model to stop training when the validation loss h, loss='sparse_categorical_crossentropy', metrics=['accuracy', min_delta = 0.1) model.compile(optimizer='adam', patience=3, validation_data=validation_batches) And here are some of the logs: Epoch 32/100 155/155 [==============================] - 21s 134m

Artificial Intelligence (AI) Development

High & Unexpected accuracy when training on one class and predicting on another

Post author By Jessica Alba
Post date February 21, 2020
No Comments on High & Unexpected accuracy when training on one class and predicting on another

I’ve been trying to train my model on fashion images (fashion_mnist) and then according to that training, make predictions on digit images (mnist). (1) According to the printed plots, the model doesn’t recognize the digit very well (which is good), but – for some reason, the printed accuracy is pretty high 97%+, which doesn’t make […]

Tags '9'] fashion_class_names = ['T-shirt/top', 'Ankle boot'] # Explore the data fashion_train_images.shape len(fashion_train_labels) fashion_train_labels fashion_test_images.shape len(fas, 'Bag', 'Coat', 'dress', 'Incorrect']) # Make predictions predictions = model.predict(digit_test_images) predictions[0] np.argmax(predictions[0]) digit_test_labels[0, 'Pullover', 'Sandal', 'Shirt', 'Sneaker', 'Trouser', (digit_test_images, (fashion_test_images, 0)) print(img.shape) predictions_single = model.predict(img) print(predictions_single) plot_value_array(1, 1, 1-test_acc]) plt.xticks(x, 1]) predicted_label = np.argmax(predictions_array) thisplot[predicted_label].set_color('red') thisplot[true_label].set_color('blue'), 10)) for i in range(25): plt.subplot(5, 100*np.max(predictions_array), 2, 28, 3, 4, 5, 6, 7, 8, 9)) pl.suptitle("Histogram for each numeric input variable") plt.savefig('digit_hist') plt.show(), activation='relu', activation='softmax') ]) # Compile the model model.compile(optimizer='adam', axis=1).hist(bins=30, but for some reason, but I receive an error about the drop function. What have I done wrong? digit_minst.drop('digit_test_labels', cmap=plt.cm.binary) plt.xlabel(fashion_class_names[fashion_train_labels[i]]) plt.show() # Build the model # Set up the layers model =, cmap=plt.cm.binary) predicted_label = np.argmax(predictions_array) if predicted_label == true_label: color = 'blue' else: colo, color="#777777") plt.ylim([0, color=color) def plot_value_array(i, correct?, digit_class_names, digit_class_names[true_label]), digit_test_labels) _ = plt.xticks(range(10), digit_test_labels) = digit_minst.load_data() (fashion_train_images, digit_train_labels), epochs=10) # Evaluate accuracy test_loss, fashion_test_labels, fashion_test_labels) = fashion_mnist.load_data() digit_class_names = ['0', fashion_train_labels), figsize=(9, height= [test_acc, I've been trying to train my model on fashion images (fashion_mnist) and then according to that training, i+1) plt.xticks([]) plt.yticks([]) plt.grid(False) plt.imshow(fashion_train_images[i], img): predictions_array, img[i] plt.grid(False) plt.xticks([]) plt.yticks([]) plt.imshow(img, keras.layers.Dense(10, keras.layers.Dense(128, loss='sparse_categorical_crossentropy', make predictions on digit images (mnist). (1) According to the printed plots, metrics=['accuracy']) # Train the model model.fit(fashion_train_images, predictions_array, predictions_single[0], rotation=45) np.argmax(predictions_single[0]) (2) I've been trying to print a histogram of the accuracies of the test examples (X=probabili, test_acc = model.evaluate(fashion_test_images, test_acc) # Plot accuracy x = np.arange(2) plt.bar(x, the model doesn't recognize the digit very well (which is good), the printed accuracy is pretty high 97%+, true_label, true_label): predictions_array, true_label[i], true_label[i] plt.grid(False) plt.xticks(range(10)) plt.yticks([]) thisplot = plt.bar(range(10), verbose=2) print('\nTest accuracy:', which doesn't make sense. What did I do wrong? #Import the Fashion MNIST dataset digit_minst = keras.datasets.mnist fashion_mnist = keras.da, Y=frequancy of the examples)

Development

.evaluate() method broken for CNN

Post author By Full Stack
Post date February 2, 2020
No Comments on .evaluate() method broken for CNN

I’ve been trying to evaluate a CNN model for a while now but I’m running into issues. Whenever I call the .evaluate method on it, my machine freezes for some time (if running on Jupyter notebook, kernel dies eventually following the freeze) and effectively no evaluation takes place. Does anyone have any idea as to […]

Tags # Flatten the results to feed into a DNN tf.keras.layers.Flatten(), 1, 2, 28, 3, activation='relu', activation='softmax') ]) model.compile(optimizer='adam', axis=1) test_data1 = tf.keras.utils.normalize(test_data, axis=1) train_dataset = tf.data.Dataset.from_tensor_slices((train_data, callbacks=[reduce_lr], epochs=NUM_EPOCHS, factor=0.2, I've been trying to evaluate a CNN model for a while now but I'm running into issues. Whenever I call the .evaluate method on it, input_shape=(28, kernel dies eventually following the freeze) and effectively no evaluation takes place. Does anyone have any idea as to what could be causing, loss='sparse_categorical_crossentropy', metrics=['accuracy']) model.fit(train_dataset, min_lr=0.001) model = tf.keras.Sequential([ tf.keras.layers.Conv2D(64, my machine freezes for some time (if running on Jupyter notebook, patience=5, test_accuracy = model.evaluate(test_dataset) print(f'The final test loss is: {test_loss}\nThe final test accuracy is: {test_accuracy}'), test_labels)) #outlining number of validation samples (picked 8% of training data) num_validation_samples = 0.08 * train_data.shape[0] num_v, tf.int64) #isolating test samples num_test_samples = test_data.shape[0] num_test_samples = tf.cast(num_test_samples, tf.int64) #setting buffer size and shuffling training data BUFFER_SIZE = 10000 train_dataset = train_dataset.shuffle(BUFFER_SIZE) #extractin, tf.keras.layers.Conv2D(64, tf.keras.layers.Dense(output_size, tf.keras.layers.Dense(standard_hidden_layer_width, tf.keras.layers.Dropout(0.5), tf.keras.layers.MaxPooling2D(2, train_labels)) test_dataset = tf.data.Dataset.from_tensor_slices((test_data, validation_data=(validation_inputs, validation_steps=validation_steps, validation_targets = next(iter(validation_dataset)) input_size = 784 output_size = 49 standard_hidden_layer_width = 512 NUM_EPOCHS = 5 VALI, validation_targets), verbose=1) test_loss

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TensorFlow with single output neuron

Post author By Full Stack
Post date January 11, 2020
No Comments on TensorFlow with single output neuron

I’m fairly new to TensorFlow and I’ve been watching tutorials that use the mnist dataset to classify images. These videos (or the ones that I watched) have 10 output neurons corresponding to the class of each image. e.g. neuron 1 is Ankle boot; neuron 2 is Shirt… These neurons output a probability but what if […]

Tags 'Ankle boot'] train_images = train_images/255.0 test_images = test_images/255.0 model = keras.Sequential([ keras.layers.Flatten(input_s, 'Bag', 'Coat', 'dress', 'Pullover', 'Sandal', 'Shirt', 'Sneaker', 'Trouser', (test_images, 28, activation='relu', activation='softmax') ]) model.compile(optimizer='adam', I’m fairly new to TensorFlow and I’ve been watching tutorials that use the mnist dataset to classify images. These videos (or the ones that, keras.layers.Dense(10, keras.layers.Dense(128, loss='sparse_categorical_crossentropy', test_labels) = data.load_data() class_names = ['T-shirt/top', train_labels

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Not able to evaluate the ML model created using Tensor Flow in PyCharm

Post author By Full Stack
Post date January 9, 2020
No Comments on Not able to evaluate the ML model created using Tensor Flow in PyCharm

I’m beginner in Python and ML. I was practising this Iris Data set to create a ML model using tensor flow 2.0. I parsed the csv and trained the model using the dataset. I’m able to get 90 % training accuracy and 91 % validation accuracy during my model creation. import tensorflow as tf import […]

Tags -1] csv_data = csv_data[:, ') target_all = csv_data[:, 0:-1] # Shuffling the input shuffled_indices = np.arange(csv_data.shape[0]) np.random.shuffle(shuffled_indices) shuffled_inputs = csv_data[, activation=tf.nn.relu)) model.add(tf.keras.layers.Dense(hidden_layer_size, activation=tf.nn.relu)) model.add(tf.keras.layers.Dense(output_size, activation=tf.nn.softmax)) model.compile(optimizer='adam', delimiter=' ', epochs=10, I'm beginner in Python and ML. I was practising this Iris Data set to create a ML model using tensor flow 2.0. I parsed the csv and trained, input_dim=input_size, loss='sparse_categorical_crossentropy', metrics=['accuracy']) model.fit(train_inputs, test_accuracy = model.evaluate(test_inputs) My problem is when i evaluate my model with test dataset Pycharm is throwing an error. I searche, train_targets, validation and test total_count = shuffled_inputs.shape[0] train_data_count = int(0.8*total_count) validation_data_count = int(0.1*total_coun, validation_data=(validation_inputs, validation_targets), verbose=2) test_loss

Development

Failed to train with tf.keras.applications.MobileNetV2

Post author By Full Stack
Post date January 1, 2020
No Comments on Failed to train with tf.keras.applications.MobileNetV2

Environment: TF2.0 Python 3.5 ubuntu 16.04 Problem: I try to use the pre-trained mobilenet_V2 but accuracy doesn’t increase: base_model = tf.keras.applications.MobileNetV2(input_shape=IMG_SHAPE, include_top=False, weights=’imagenet’) The script is copied from the tutorial of the tensorflow 2.0(https://www.tensorflow.org/tutorials/images/transfer_learning?hl=zh-cn) The only change I made is the dataset which feed into the network. The original code makes binary classification between dogs […]

Tags 'Division', 'image' ];, "tf_flowers". Please note that, (IMG_SIZE, 1, 1) splits = tfds.Split.TRAIN.subsplit(weighted=SPLIT_WEIGHTS) (raw_train, 2) image = tf.cast(image, 3) # Create the base model from the pre-trained model MobileNet V2 base_model = tf.keras.applications.MobileNetV2(input_shape=IMG_SHAPE, accuracy0 = model.evaluate(validation_batches, and everything works. However, as_supervised=True) get_label_name = metadata.features['label'].int2str print(raw_train) print(raw_validation) print(raw_test) IMG_SIZE = 1, Environment: TF2.0 Python 3.5 ubuntu 16.04 Problem: I try to use the pre-trained mobilenet_V2 but accuracy doesn't increase: base_model = t, epochs=initial_epochs, feature_batch.shape) global_average_layer = tf.keras.layers.GlobalAveragePooling2D() feature_batch_average = global_average_layer(feature_ba, global_average_layer, I used the correct loss function and metrics. Here is the code: from __future__ import absolute_import, image_batch.shape) print("feature batch size:", IMG_SIZE)) return image, include_top=False, label train = raw_train.map(format_example) validation = raw_validation.map(format_example) test = raw_test.map(format_example) BATCH_SIZE, label_batch in train_batches.take(1): pass feature_batch = base_model(image_batch) base_model.trainable = False base_model.summary() pr, label): if image.shape[-1] == 1: image = tf.concat([image, loss='sparse_categorical_crossentropy', metadata = tfds.load( 'mnist', metrics=['sparse_categorical_accuracy']) num_train, num_test = ( metadata.splits['train'].num_examples*weight/10 for weight in SPLIT_WEIGHTS ) initial_epochs = 10 steps_per_epoch = round(n, num_val, prediction_layer ]) tf.keras.backend.set_learning_phase(True) base_learning_rate = 0.01 model.compile(optimizer=tf.keras.optimizers.RMSprop, print_function, raw_test), raw_validation, split=list(splits), steps = validation_steps) print("initial loss: {:.2f}".format(loss0)) print("initial accuracy: {:.2f}".format(accuracy0)) history = model.f, tf.float32) image = (image/127.5) - 1 image = tf.image.resize(image, the accuracy never increases while using multi-classes datasets like: "mnist", unicode_literals import os import numpy as np import tensorflow as tf keras = tf.keras import tensorflow_datasets as tfds # tfds.disable_pro, validation_data=validation_batches), weights='imagenet') for image_batch, weights='imagenet') The script is copied from the tutorial of the tensorflow 2.0(https://www.tensorflow.org/tutorials/images/transfer_learni, with_info=True

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How to sovle audio signal problem by using 1D conv neural network in keras

Post author By Full Stack
Post date December 29, 2019
No Comments on How to sovle audio signal problem by using 1D conv neural network in keras

I have a dataset (it is given and not a problem). all_speakers = np.unique([os.path.basename(i).split(‘_’)[1] for i in fsdd]) np.random.shuffle(all_speakers) train_speakers = all_speakers[:2] test_speakers = all_speakers[2:] print(“All speakers:”, all_speakers) print(“Train speakers:”, train_speakers) print(“Test speakers:”, test_speakers) train_files = [ i for i in fsdd if os.path.basename(i).split(‘_’)[1] in train_speakers ] test_files = [i for i in fsdd if […]

Tags ) n_epoch = 12 model.fit(x=train.repeat(n_epoch)) model.evaluate(test), 40))) model.add(keras.layers.MaxPool1D(pool_size=(2), activation='relu', activation='relu')) model.add(keras.layers.Dense(10, activation='relu')) model.add(keras.layers.Dense(512, activation='relu')) model.add(keras.layers.MaxPool1D(pool_size=(2), activation='softmax')) model.compile( optimizer='adam', all_speakers) print("Train speakers:", and get the accuracy over 90%. My model is not good enough, and I don't think it is an overfitting problem. model = keras.Sequential() model.add(keras.layers.Conv1D(32, I have a dataset (it is given and not a problem). all_speakers = np.unique([os.path.basename(i).split('_')[1] for i in fsdd]) np.random.shuf, input_shape=(300, kernel_size=(5, loss='sparse_categorical_crossentropy', metrics=['accuracy', nicolas test set is theo model.add(keras.layers.Flatten()) model.add(keras.layers.Dense(1024, strides=(2))) #training set is jackson, strides=(2))) model.add(keras.layers.Conv1D(64, test_speakers) train_files = [ i for i in fsdd if os.path.basename(i).split('_')[1] in train_speakers ] test_files = [i for i in fsdd if, train_speakers) print("Test speakers:", training=False) The outcome is: All speakers: ['nicolas' 'theo' 'jackson'] Train speakers: ['nicolas' 'theo'] Test speakers: ['jackson'], training=True) test = create_audio_dataset(test_files

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Tensorflow: How to use tf.keras.metrics in multiclass classification?

Post author By Full Stack
Post date December 17, 2019
No Comments on Tensorflow: How to use tf.keras.metrics in multiclass classification?

I want to use some of these metrics when training my neural network: METRICS = [ keras.metrics.TruePositives(name=’tp’), keras.metrics.FalsePositives(name=’fp’), keras.metrics.TrueNegatives(name=’tn’), keras.metrics.FalseNegatives(name=’fn’), keras.metrics.Precision(name=’precision’), keras.metrics.Recall(name=’recall’), keras.metrics.CategoricalAccuracy(name=’acc’), keras.metrics.AUC(name=’auc’), ] BATCH_SIZE = 1024 SHUFFLE_BUFFER_SIZE = 4000 train_dataset = tf.data.Dataset.from_tensor_slices((sent_vectors, labels)) train_dataset = train_dataset.shuffle(SHUFFLE_BUFFER_SIZE).batch(BATCH_SIZE) model = tf.keras.Sequential() model.add(tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(embed_dim))) for units in [256, 256]: model.add(tf.keras.layers.Dense(units, activation=’relu’)) model.add(tf.keras.layers.Dense(4, activation=’softmax’)) model.compile(optimizer=’adam’, loss=’sparse_categorical_crossentropy’, metrics=METRICS) model.fit( […]

Tags _, _ = ragged_assert_compatible_and_get_flat_values([y_pred, _ = self._maybe_define_function(args, ----> 3 epochs=10) /opt/conda/lib/python3.7/site-packages/tensorflow_core/python/keras/engine/training.py in fit(self, ---> 74 skip_target_masks=model._prepare_skip_target_masks()) 75 76 # Add metric results from the `add_metric` metrics., ---> 86 distributed_function(input_fn)) 87 88 return execution_function /opt/conda/lib/python3.7/, --> 264 output_loss_metrics=model._output_loss_metrics) 265 266 if reset_metrics: /opt/conda/lib/python3.7/site-packages/te, --> 324 total_epochs=epochs) 325 cbks.make_logs(model, --> 728 use_multiprocessing=use_multiprocessing) 729 730 def evaluate(self, --> 883 sample_weight=sample_weight) 884 885 def result(self): /opt/conda/lib/python3.7/site-packages/tensorflow_core/pyt, -> 2041 capture_by_value=self._capture_by_value), -> 2063 target, :iterator?, .outs, '" .$Class_ID."', "outputs": {}, "Value"), ] BATCH_SIZE = 1024 SHUFFLE_BUFFER_SIZE = 4000 train_dataset = tf.data.Dataset.from_tensor_slices((sent_vectors, {"mode", **func_kwargs) 916 917 # invariant: `func_outputs` contains only Tensors, **kwargs) # pylint: disable=not-callable 177 with ops.control_dependencies([update_op]): 178 result_t = self.result(), **kwargs) 1133 with strategy.scope(): 1134 return strategy.extended.call_for_each_replica(fn, **kwargs) 1136 1137 /opt/conda/lib/python3.7/site-packages/tensorflow_core/python/keras/metrics.py in replica_local_fn(*args, **kwargs) 174 def replica_local_fn(*args, **kwargs) 1846 if self.input_signature: 1847 args, **kwargs) 191 from tensorflow.python.keras.distribute import distributed_training_utils # pylint:disable=g-import-not-at-top 192, **kwargs) 194 195 @property /opt/conda/lib/python3.7/site-packages/tensorflow_core/python/keras/distribute/distributed_training_u, **kwargs) 2133 2134 def _reduce_to(self, **kwargs) 290 def wrapper(*args, **kwargs) 293 294 if inspect.isfunction(func) or inspect.ismethod(func): /opt/conda/lib/python3.7/site-packages/tensorflow_core/p, **kwargs) 322 mode=ModeKeys.TRAIN, **kwargs) 726 max_queue_size=max_queue_size, **kwargs) 73 74 with tf_utils.graph_context_for_symbolic_tensors(*args, **kwargs) 76 if update_op is not None: # update_op will be None in eager execution. 77 metric_obj.add_update(update_op), **kwargs): ---> 75 update_op = update_state_fn(*args, **kwargs): 175 """Updates the state of the metric in a replica-local context.""" --> 176 update_op = self.update_state(*args, **kwargs): 291 with ag_ctx.ControlStatusCtx(status=ag_ctx.Status.DISABLED): --> 292 return func(*args, **kwds) 356 # __wrapped__ allows AutoGraph to swap in a converted function. We give 357 # the function a weak referen, **kwds) 359 weak_wrapped_fn = weakref.ref(wrapped_fn) 360 /opt/conda/lib/python3.7/site-packages/tensorflow_core/python/keras/e, **kwds) 455 456 tracing_count = self._get_tracing_count() --> 457 result = self._call(*args, **kwds) 458 if tracing_count == self._get_tracing_count(): 459 self._call_counter.called_without_tracing() /opt/conda/lib/, **kwds) 501 # This is the first call of __call__, **kwds)) 409 410 def invalid_creator_scope(*unused_args, **unused_kwds): /opt/conda/lib/python3.7/site-packages/tensorflow_core/python/eager/function.py in _get_concrete_function_internal_garbage_c, *args, /opt/conda/lib/python3.7/site-packages/tensorflow_core/python/eager/def_function.py in wrapped_fn(*args, /opt/conda/lib/python3.7/site-packages/tensorflow_core/python/keras/engine/training_eager.py in _eager_metrics_fn(model, /opt/conda/lib/python3.7/site-packages/tensorflow_core/python/keras/engine/training_v2.py in fit(self, 1) are incompatible, 1) are incompatible. I believe this is because I am doing multiclass classification on 4 classes but the metrics are calculated based on bina, 1). I have 4 labels: 0-3. Stacktrace: ValueErrorTraceback (most recent call last) in 1 model, 2040 override_flat_arg_shapes=override_flat_arg_shapes, 2042 self._function_attributes, 2043 # Tell the ConcreteFunction to clean up its graph once it goes out of /opt/conda/lib/python3.7/site-packages/tensorflow_core/py, 2131 replica_id_in_sync_group=constant_op.constant(0, 256]: model.add(tf.keras.layers.Dense(units, 263 sample_weights=sample_weights, 277 sample_weight) --> 278 y_pred.shape.assert_is_compatible_with(y_true.shape, 300) and labels=(number_examples, 323 training_context=training_context, 4) and (None, 65, 727 workers=workers, 73 return_weighted_and_unweighted_metrics=True, 759 convert_by_default=False) --> 760 return self._extended.call_for_each_replica(fn, 882 thresholds=self.thresholds, activation='relu')) model.add(tf.keras.layers.Dense(4, activation='softmax')) model.compile(optimizer='adam', add_control_dependencies, add_initializers_to) 406 self._concrete_stateful_fn = ( 407 self._stateful_fn._get_concrete_function_internal_garbage_col, add_initializers_to=initializer_map) 504 finally: 505 # At this point we know that the initialization is complete (or less, ag_ctx.control_status_ctx(), arg_names, args=(model, args=args), autograph, autograph_options, axis): /opt/conda/lib/python3.7/site-packages/tensorflow_core/python/distribute/distribute_lib.py in call_for_each_replica(self, batch_size, callbacks, capture_by_value, class_weight, collections, CompositeTensors, dataset_size, destinations): /opt/conda/lib/python3.7/site-packages/tensorflow_core/python/autograph/impl/api.py in wrapper(*args, dtypes.int32)): -> 2132 return fn(*args, epoch_logs, epochs, epochs=100) But I get Shapes (None, errors.OutOfRangeError): 125 # TODO(kaftan): File bug about tf function and errors.OutOfRangeError? /opt/conda/lib/python3.7/sit, execution_function, fn, func_graph, I want to use some of these metrics when training my neural network: METRICS = [ keras.metrics.TruePositives(name='tp'), initial_epoch, inputs, keras.metrics.AUC(name='auc'), keras.metrics.CategoricalAccuracy(name='acc'), keras.metrics.FalseNegatives(name='fn'), keras.metrics.FalsePositives(name='fp'), keras.metrics.Precision(name='precision'), keras.metrics.Recall(name='recall'), keras.metrics.TrueNegatives(name='tn'), kwargs, kwargs /opt/conda/lib/python3.7/site-packages/tensorflow_core/python/eager/function.py in _create_graph_function(self, kwargs = None, kwargs) -> 1135 return fn(*args, kwargs) 1785 kwargs = {} 1786 with self._container_strategy().scope(): -> 1787 return self._call_for_each_replica(fn, kwargs) 1788 1789 def _call_for_each_replica(self, kwargs) 1849 return graph_function 1850 /opt/conda/lib/python3.7/site-packages/tensorflow_core/python/eager/function.py in _maybe, kwargs) 2130 self._container_strategy(), kwargs) 2148 graph_function = self._function_cache.primary.get(cache_key, kwargs) 2151 self._function_cache.primary[cache_key] = graph_function 2152 return graph_function, kwargs) 758 fn = autograph.tf_convert(fn, kwargs): /opt/conda/lib/python3.7/site-packages/tensorflow_core/python/distribute/distribute_lib.py in _call_for_each_replica(self, kwargs=kwargs) 761 762 def reduce(self, kwds, labels)) train_dataset = train_dataset.shuffle(SHUFFLE_BUFFER_SIZE).batch(BATCH_SIZE) model = tf.keras.Sequential() model.add(tf.keras.layer, loss='sparse_categorical_crossentropy', mask, mask) 1065 1066 if y_pred is not None: -> 1067 return metric_fn(y_true, mask=mask) 2015 metric_results.append(metric_result) 2016 return metric_results /opt/conda/lib/python3.7/site-packages/ten, masks, masks) 72 masks=masks, masks=masks) 316 total_loss = nest.flatten(total_loss) 317 return {'total_loss': total_loss, max_queue_size, metrics_dict, metrics=METRICS) model.fit( train_dataset, mode=mode, ModeKeys.TRAIN) 326 /opt/conda/lib/python3.7/site-packages/tensorflow_core/python/keras/engine/training_v2.py in run_one_epoch(model, model, None -> 1848 graph_function, None) 2149 if graph_function is None: -> 2150 graph_function = self._create_graph_function(args, not just at the end of the fitting process. Additional infos: My input data are numpy arrays with the shape sent_vectors.shape = (number_exa, num_samples, op_return_value, other) 1113 """ 1114 if not self.is_compatible_with(other): -> 1115 raise ValueError("Shapes %s and %s are incompatible", other): ValueError: Shapes (None, other)) 1116 1117 def most_specific_compatible_shape(self, Output, output_loss_metrics) 313 outs = [outs] 314 metrics_results = _eager_metrics_fn( --> 315 model, output_mask)) 2064 if return_weighted_and_unweighted_metrics or return_weighted_metrics: 2065 metric_results.extend(, override_flat_arg_shapes) 2039 arg_names=arg_names, override_flat_arg_shapes) 913 converted_func) 914 --> 915 func_outputs = python_func, python_func, reduce_op, reset_metrics) 262 y, return_weighted_and_unweighted_metrics) 2061 metric_results.extend( 2062 self._handle_per_output_metrics(self._, return_weighted_metrics, sample_weight, sample_weight) 276 y_true], sample_weight) 881 y_pred, sample_weight=weights) /opt/conda/lib/python3.7/site-packages/tensorflow_core/python/keras/metrics.py in __call__(self, sample_weight=weights) 1068 # `Mean` metric only takes a single value. 1069 return metric_fn(y_true, sample_weights, sample_weights)) 74 # Out of PerReplica outputs reduce or pick values to return. 75 all_outputs = dist_utils.unwrap_output_, sample_weights=sample_weights, shuffle, signature, size=current_batch_size) as batch_logs: 122 try: --> 123 batch_outs = execution_function(iterator) 124 except (St, skip_target_masks, so we have to initialize. 502 initializer_map = object_identity.ObjectIdentityDictionary() --> 503 self._initialize(args, steps_per_epoch, strategy, targets, thresholds, top_k, total_epochs) 121 step=step, training_context, training_result, use_multiprocessing, validation_data, validation_freq, validation_split, validation_steps, verbose, weights, weights) 2012 with K.name_scope(metric_name): 2013 metric_result = training_utils.call_metric_function( -> 2014, weights=weights, workers, X, y, y_pred, y_true

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Tensorflow 2.0 how to export predictions and training-mode from Keras model

Post author By Full Stack
Post date December 3, 2019
No Comments on Tensorflow 2.0 how to export predictions and training-mode from Keras model

I have a model sub-classed from tf.keras.model . I have write a call -and predict methods. when I export the model it seems that only the output from call method is serialized. The simple code to illustrate the problem is below class SimpleModel(tf.keras.Model): def __init__(self): super(SimpleModel, self).__init__() self.layer1 = keras.layers.Flatten(input_shape=(28, 28)) self.layer2 = keras.layers.Dense(128, activation=’relu’) […]

Tags 'Ankle boot'] model = SimpleModel() model.compile(optimizer='adam', 'Bag', 'Coat', 'dress', 'Pullover', 'Sandal', 'Shirt', 'Sneaker', 'tf_test', 'Trouser', "mode": training_mode}, (test_images, 10) name: StatefulPartitionedCall:0 Method name is: tensorflow/serving/predict The output is exactly same without the predict method., 28, 28) name: serving_default_input_1:0 The given SavedModel SignatureDef contains the following output(s): outputs['output_1'] tensor_in, 28)) self.layer2 = keras.layers.Dense(128, activation='relu') self.dropout = keras.layers.Dropout(0.5) self.layer3 = keras.layers.Dense(10, activation='softmax') def call(self, epochs=10) model.save('tf_test', I have a model sub-classed from tf.keras.model . I have write a call -and predict methods. when I export the model it seems that only the out, inputs={"x": x, loss='sparse_categorical_crossentropy', metrics=['accuracy']) model.fit(train_images, outputs={"predictions": predictions}), save_format='tf') When I inspect the saved model using the saved-model-cli the output is as below The given SavedModel SignatureDef contain, self).__init__() self.layer1 = keras.layers.Flatten(input_shape=(28, test_labels) = fashion_mnist.load_data() class_names = ['T-shirt/top', train_labels, training=False): x = self.layer1(x) x = self.layer2(x) if training: x = self.dropout(x) return self.layer3(x) def p, X, x): return tf.argmax(self(x)) fashion_mnist = keras.datasets.fashion_mnist (train_images