Python keras.losses.categorical_crossentropy() Examples
The following are 30 code examples for showing how to use keras.losses.categorical_crossentropy(). These examples are extracted from open source projects. You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example.
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Example 1
Project: Jtyoui Author: jtyoui File: HandWritingRecognition.py License: MIT License | 6 votes |
def nn_model(): (x_train, y_train), _ = mnist.load_data() # 归一化 x_train = x_train.reshape(x_train.shape[0], -1) / 255. # one-hot y_train = np_utils.to_categorical(y=y_train, num_classes=10) # constant(value=1.)自定义常数,constant(value=1.)===one() # 创建模型:输入784个神经元,输出10个神经元 model = Sequential([ Dense(units=200, input_dim=784, bias_initializer=constant(value=1.), activation=tanh), Dense(units=100, bias_initializer=one(), activation=tanh), Dense(units=10, bias_initializer=one(), activation=softmax), ]) opt = SGD(lr=0.2, clipnorm=1.) # 优化器 model.compile(optimizer=opt, loss=categorical_crossentropy, metrics=['acc', 'mae']) # 编译 model.fit(x_train, y_train, batch_size=64, epochs=20, callbacks=[RemoteMonitor()]) model_save(model, './model.h5')
Example 2
Project: hyperspectral_deeplearning_review Author: mhaut File: cnn3d.py License: GNU General Public License v3.0 | 6 votes |
def get_model_compiled(shapeinput, num_class, w_decay=0, lr=1e-3): clf = Sequential() clf.add(Conv3D(32, kernel_size=(5, 5, 24), input_shape=shapeinput)) clf.add(BatchNormalization()) clf.add(Activation('relu')) clf.add(Conv3D(64, (5, 5, 16))) clf.add(BatchNormalization()) clf.add(Activation('relu')) clf.add(MaxPooling3D(pool_size=(2, 2, 1))) clf.add(Flatten()) clf.add(Dense(300, kernel_regularizer=regularizers.l2(w_decay))) clf.add(BatchNormalization()) clf.add(Activation('relu')) clf.add(Dense(num_class, activation='softmax')) clf.compile(loss=categorical_crossentropy, optimizer=Adam(lr=lr), metrics=['accuracy']) return clf
Example 3
Project: hyperspectral_deeplearning_review Author: mhaut File: transfer_learning.py License: GNU General Public License v3.0 | 6 votes |
def get_model_compiled(args, inputshape, num_class): model = Sequential() if args.arch == "CNN1D": model.add(Conv1D(20, (24), activation='relu', input_shape=inputshape)) model.add(MaxPooling1D(pool_size=5)) model.add(Flatten()) model.add(Dense(100)) elif "CNN2D" in args.arch: model.add(Conv2D(50, kernel_size=(5, 5), input_shape=inputshape)) model.add(Activation('relu')) model.add(Conv2D(100, (5, 5))) model.add(Activation('relu')) model.add(MaxPooling2D(pool_size=(2, 2))) model.add(Flatten()) model.add(Dense(100)) elif args.arch == "CNN3D": model.add(Conv3D(32, kernel_size=(5, 5, 24), input_shape=inputshape)) model.add(BatchNormalization()) model.add(Activation('relu')) model.add(Conv3D(64, (5, 5, 16))) model.add(BatchNormalization()) model.add(Activation('relu')) model.add(MaxPooling3D(pool_size=(2, 2, 1))) model.add(Flatten()) model.add(Dense(300)) if args.arch != "CNN2D": model.add(BatchNormalization()) model.add(Activation('relu')) model.add(Dense(num_class, activation='softmax')) model.compile(loss=categorical_crossentropy, optimizer=Adam(args.lr1), metrics=['accuracy']) return model
Example 4
Project: keras-contrib Author: keras-team File: crf_losses.py License: MIT License | 6 votes |
def crf_loss(y_true, y_pred): """General CRF loss function depending on the learning mode. # Arguments y_true: tensor with true targets. y_pred: tensor with predicted targets. # Returns If the CRF layer is being trained in the join mode, returns the negative log-likelihood. Otherwise returns the categorical crossentropy implemented by the underlying Keras backend. # About GitHub If you open an issue or a pull request about CRF, please add `cc @lzfelix` to notify Luiz Felix. """ crf, idx = y_pred._keras_history[:2] if crf.learn_mode == 'join': return crf_nll(y_true, y_pred) else: if crf.sparse_target: return sparse_categorical_crossentropy(y_true, y_pred) else: return categorical_crossentropy(y_true, y_pred)
Example 5
Project: nlp_xiaojiang Author: yongzhuo File: keras_bert_layer.py License: MIT License | 6 votes |
def crf_loss(y_true, y_pred): """General CRF loss function depending on the learning mode. # Arguments y_true: tensor with true targets. y_pred: tensor with predicted targets. # Returns If the CRF layer is being trained in the join mode, returns the negative log-likelihood. Otherwise returns the categorical crossentropy implemented by the underlying Keras backend. # About GitHub If you open an issue or a pull request about CRF, please add `cc @lzfelix` to notify Luiz Felix. """ crf, idx = y_pred._keras_history[:2] if crf.learn_mode == 'join': return crf_nll(y_true, y_pred) else: if crf.sparse_target: return sparse_categorical_crossentropy(y_true, y_pred) else: return categorical_crossentropy(y_true, y_pred) # crf_marginal_accuracy, crf_viterbi_accuracy
Example 6
Project: talos Author: autonomio File: params.py License: MIT License | 6 votes |
def iris(): from keras.optimizers import Adam, Nadam from keras.losses import logcosh, categorical_crossentropy from keras.activations import relu, elu, softmax # here use a standard 2d dictionary for inputting the param boundaries p = {'lr': (0.5, 5, 10), 'first_neuron': [4, 8, 16, 32, 64], 'hidden_layers': [0, 1, 2, 3, 4], 'batch_size': (2, 30, 10), 'epochs': [2], 'dropout': (0, 0.5, 5), 'weight_regulizer': [None], 'emb_output_dims': [None], 'shapes': ['brick', 'triangle', 0.2], 'optimizer': [Adam, Nadam], 'losses': [logcosh, categorical_crossentropy], 'activation': [relu, elu], 'last_activation': [softmax]} return p
Example 7
Project: Federated-Learning-Mini-Framework Author: gaborvecsei File: models.py License: MIT License | 6 votes |
def create_model(input_shape: tuple, nb_classes: int, init_with_imagenet: bool = False, learning_rate: float = 0.01): weights = None if init_with_imagenet: weights = "imagenet" model = VGG16(input_shape=input_shape, classes=nb_classes, weights=weights, include_top=False) # "Shallow" VGG for Cifar10 x = model.get_layer('block3_pool').output x = layers.Flatten(name='Flatten')(x) x = layers.Dense(512, activation='relu')(x) x = layers.Dense(nb_classes)(x) x = layers.Softmax()(x) model = models.Model(model.input, x) loss = losses.categorical_crossentropy optimizer = optimizers.SGD(lr=learning_rate, decay=0.99) model.compile(optimizer, loss, metrics=["accuracy"]) return model
Example 8
Project: EmoPy Author: thoughtworksarts File: neuralnets.py License: GNU Affero General Public License v3.0 | 6 votes |
def _init_model(self): """ Initialize base model from Keras and add top layers to match number of training emotions labels. :return: """ base_model = self._get_base_model() top_layer_model = base_model.output top_layer_model = GlobalAveragePooling2D()(top_layer_model) top_layer_model = Dense(1024, activation='relu')(top_layer_model) prediction_layer = Dense(output_dim=len(self.emotion_map.keys()), activation='softmax')(top_layer_model) model = Model(input=base_model.input, output=prediction_layer) print(model.summary()) for layer in base_model.layers: layer.trainable = False model.compile(optimizer='rmsprop', loss='categorical_crossentropy', metrics=['accuracy']) self.model = model
Example 9
Project: EmoPy Author: thoughtworksarts File: neuralnets.py License: GNU Affero General Public License v3.0 | 6 votes |
def fit(self, features, labels, validation_split, epochs=50): """ Trains the neural net on the data provided. :param features: Numpy array of training data. :param labels: Numpy array of target (label) data. :param validation_split: Float between 0 and 1. Percentage of training data to use for validation :param epochs: Max number of times to train over dataset. """ self.model.fit(x=features, y=labels, epochs=epochs, verbose=1, callbacks=[ReduceLROnPlateau(), EarlyStopping(patience=3)], validation_split=validation_split, shuffle=True) for layer in self.model.layers[:self._NUM_BOTTOM_LAYERS_TO_RETRAIN]: layer.trainable = False for layer in self.model.layers[self._NUM_BOTTOM_LAYERS_TO_RETRAIN:]: layer.trainable = True self.model.compile(optimizer='sgd', loss='categorical_crossentropy', metrics=['accuracy']) self.model.fit(x=features, y=labels, epochs=50, verbose=1, callbacks=[ReduceLROnPlateau(), EarlyStopping(patience=3)], validation_split=validation_split, shuffle=True)
Example 10
Project: Jtyoui Author: jtyoui File: HandWritingRecognition.py License: MIT License | 5 votes |
def cnn_model(): (x_train, y_train), _ = mnist.load_data() # 归一化 x_train = x_train.reshape(-1, 28, 28, 1) / 255. # one-hot y_train = np_utils.to_categorical(y=y_train, num_classes=10) model = Sequential([ # input_shape:输入平面,就在第一个位置设置 # filters:卷积核、滤波器 # kernel_size:卷积核大小 # strides:步长 # padding有两种方式:same/valid # activation:激活函数 Convolution2D(input_shape=(28, 28, 1), filters=32, kernel_size=5, strides=1, padding='same', activation=relu), MaxPool2D(pool_size=2, strides=2, padding='same'), Convolution2D(filters=64, kernel_size=5, padding='same', activation=relu), MaxPool2D(pool_size=2, trainable=2, padding='same'), Flatten(), # 扁平化 Dense(units=1024, activation=relu), Dropout(0.5), Dense(units=10, activation=softmax), ]) opt = Adam(lr=1e-4) model.compile(optimizer=opt, loss=categorical_crossentropy, metrics=['accuracy']) model.fit(x=x_train, y=y_train, batch_size=64, epochs=20, callbacks=[RemoteMonitor()]) model_save(model, './model.h5')
Example 11
Project: Jtyoui Author: jtyoui File: HandWritingRecognition.py License: MIT License | 5 votes |
def rnn_model(): (x_train, y_train), _ = mnist.load_data() # 归一化 x_train = x_train / 255. # one-hot y_train = np_utils.to_categorical(y=y_train, num_classes=10) model = Sequential([ SimpleRNN(units=50, input_shape=(28, 28)), Dense(units=10, activation=softmax), ]) opt = RMSprop(lr=1e-4) model.compile(optimizer=opt, loss=categorical_crossentropy, metrics=['accuracy']) model.fit(x=x_train, y=y_train, batch_size=64, epochs=20, callbacks=[RemoteMonitor()]) model_save(model, './model.h5')
Example 12
Project: image-segmentation-keras Author: divamgupta File: train.py License: MIT License | 5 votes |
def masked_categorical_crossentropy(gt, pr): from keras.losses import categorical_crossentropy mask = 1 - gt[:, :, 0] return categorical_crossentropy(gt, pr) * mask
Example 13
Project: keras-gcnn Author: basveeling File: test_model_saving.py License: MIT License | 5 votes |
def test_functional_model_saving(): img_rows, img_cols = 32, 32 img_channels = 3 # Parameters for the DenseNet model builder img_dim = (img_channels, img_rows, img_cols) if K.image_data_format() == 'channels_first' else ( img_rows, img_cols, img_channels) depth = 40 nb_dense_block = 3 growth_rate = 3 # number of z2 maps equals growth_rate * group_size, so keep this small. nb_filter = 16 dropout_rate = 0.0 # 0.0 for data augmentation conv_group = 'D4' # C4 includes 90 degree rotations, D4 additionally includes reflections in x and y axis. use_gcnn = True # Create the model (without loading weights) model = GDenseNet(mc_dropout=False, padding='same', nb_dense_block=nb_dense_block, growth_rate=growth_rate, nb_filter=nb_filter, dropout_rate=dropout_rate, weights=None, input_shape=img_dim, depth=depth, use_gcnn=use_gcnn, conv_group=conv_group) model.compile(loss=losses.categorical_crossentropy, optimizer=optimizers.Adam(), metrics=[metrics.categorical_accuracy]) x = np.random.random((1, 32, 32, 3)) y = np.random.randint(0, 10, 1) y = np_utils.to_categorical(y, 10) model.train_on_batch(x, y) out = model.predict(x) _, fname = tempfile.mkstemp('.h5') save_model(model, fname) model = load_model(fname) os.remove(fname) out2 = model.predict(x) assert_allclose(out, out2, atol=1e-05)
Example 14
Project: hyperspectral_deeplearning_review Author: mhaut File: cnn2d.py License: GNU General Public License v3.0 | 5 votes |
def get_model_compiled(shapeinput, num_class, w_decay=0): clf = Sequential() clf.add(Conv2D(50, kernel_size=(5, 5), input_shape=shapeinput)) clf.add(Activation('relu')) clf.add(Conv2D(100, (5, 5))) clf.add(Activation('relu')) clf.add(MaxPooling2D(pool_size=(2, 2))) clf.add(Flatten()) clf.add(Dense(100, kernel_regularizer=regularizers.l2(w_decay))) clf.add(Activation('relu')) clf.add(Dense(num_class, activation='softmax')) clf.compile(loss=categorical_crossentropy, optimizer=Adam(), metrics=['accuracy']) return clf
Example 15
Project: hyperspectral_deeplearning_review Author: mhaut File: cnn1d.py License: GNU General Public License v3.0 | 5 votes |
def get_model_compiled(bands, num_class): clf = Sequential() clf.add(Conv1D(20, (24), activation='relu', input_shape=(bands,1))) clf.add(MaxPooling1D(pool_size=5)) clf.add(Flatten()) clf.add(Dense(100)) clf.add(BatchNormalization()) clf.add(Activation('relu')) clf.add(Dense(num_class, activation='softmax')) clf.compile(loss=categorical_crossentropy, optimizer=Adam(), metrics=['accuracy']) return clf
Example 16
Project: hyperspectral_deeplearning_review Author: mhaut File: recurrent.py License: GNU General Public License v3.0 | 5 votes |
def get_model_compiled(feat_size, seq_len, num_class, type_func): if type_func == "RNN": func = SimpleRNN elif type_func == "GRU": func = CuDNNGRU elif type_func == "LSTM": func = CuDNNLSTM else: print("NOT RECURRENT FUNC") clf = Sequential() clf.add(func(64, return_sequences=True, input_shape=(feat_size, seq_len))) clf.add(func(64, return_sequences=True)) clf.add(Flatten()) clf.add(Dense(num_class, activation='softmax')) clf.compile(loss=categorical_crossentropy, optimizer=Adam(), metrics=['accuracy']) return clf
Example 17
Project: hyperspectral_deeplearning_review Author: mhaut File: mlp.py License: GNU General Public License v3.0 | 5 votes |
def get_model_compiled(n_bands, num_class): clf = Sequential() clf.add(Dense(int(n_bands*2/3.)+10, activation='relu', input_shape=(n_bands,))) clf.add(Dense(num_class, activation='softmax')) clf.compile(loss=categorical_crossentropy, optimizer=Adam(), metrics=['accuracy']) return clf
Example 18
Project: coremltools Author: apple File: test_keras2.py License: BSD 3-Clause "New" or "Revised" License | 5 votes |
def test_updatable_model_flag_off(self): """ Test to ensure that when respect_trainable is off, then we will ignore any 'trainable' layers of the original network. """ import coremltools from keras.layers import Dense from keras.losses import categorical_crossentropy from keras.optimizers import SGD input = ["data"] output = ["output"] # First, set respect_trainable to False and then check to make sure the # converted model is NOT updatable. not_updatable = Sequential() not_updatable.add(Dense(128, input_shape=(16,))) # layer is updatable, but the flag during convert is false, so that bit # must get dropped on the floor. not_updatable.add(Dense(10, name="foo", activation="softmax", trainable=True)) not_updatable.compile( loss=categorical_crossentropy, optimizer=SGD(lr=0.01), metrics=["accuracy"] ) cml = coremltools.converters.keras.convert( not_updatable, input, output, respect_trainable=False ) spec = cml.get_spec() self.assertFalse(spec.isUpdatable) layers = spec.neuralNetwork.layers self.assertIsNotNone(layers[1].innerProduct) self.assertTrue(layers[1].innerProduct) self.assertFalse(layers[1].isUpdatable)
Example 19
Project: coremltools Author: apple File: test_keras2.py License: BSD 3-Clause "New" or "Revised" License | 5 votes |
def test_updatable_model_flag_cce_sgd(self): """ Test to ensure that respect_trainable is honored during convert of a model with categorical cross entropy loss and SGD optimizer. """ import coremltools from keras.layers import Dense from keras.losses import categorical_crossentropy from keras.optimizers import SGD input = ["data"] output = ["output"] # This should result in an updatable model. updatable = Sequential() updatable.add(Dense(128, input_shape=(16,))) updatable.add(Dense(10, name="foo", activation="softmax", trainable=True)) updatable.compile( loss=categorical_crossentropy, optimizer=SGD(lr=1.0), metrics=["accuracy"] ) cml = coremltools.converters.keras.convert( updatable, input, output, respect_trainable=True ) spec = cml.get_spec() self.assertTrue(spec.isUpdatable) layers = spec.neuralNetwork.layers self.assertIsNotNone(layers[1].innerProduct) self.assertTrue(layers[1].innerProduct) self.assertTrue(layers[1].isUpdatable) self.assertEqual(len(spec.neuralNetwork.updateParams.lossLayers), 1) sgdopt = spec.neuralNetwork.updateParams.optimizer.sgdOptimizer self.assertEqual(sgdopt.learningRate.defaultValue, 1.0) self.assertEqual(sgdopt.miniBatchSize.defaultValue, 16) self.assertEqual(sgdopt.momentum.defaultValue, 0.0)
Example 20
Project: coremltools Author: apple File: test_keras2.py License: BSD 3-Clause "New" or "Revised" License | 5 votes |
def test_updatable_model_flag_functional(self): """ Test to ensure that respect_trainable is honored during convert of a Keras model defined via the Keras functional API. """ import coremltools from keras.layers import Dense, Input from keras.losses import categorical_crossentropy from keras.optimizers import SGD input = ["data"] output = ["output"] # This should result in an updatable model. inputs = Input(shape=(16,)) d1 = Dense(128)(inputs) d2 = Dense(10, name="foo", activation="softmax", trainable=True)(d1) kmodel = Model(inputs=inputs, outputs=d2) kmodel.compile( loss=categorical_crossentropy, optimizer=SGD(lr=1.0), metrics=["accuracy"] ) cml = coremltools.converters.keras.convert( kmodel, input, output, respect_trainable=True ) spec = cml.get_spec() self.assertTrue(spec.isUpdatable) layers = spec.neuralNetwork.layers self.assertIsNotNone(layers[1].innerProduct) self.assertTrue(layers[1].innerProduct) self.assertTrue(layers[1].isUpdatable) self.assertEqual(len(spec.neuralNetwork.updateParams.lossLayers), 1) sgdopt = spec.neuralNetwork.updateParams.optimizer.sgdOptimizer self.assertEqual(sgdopt.learningRate.defaultValue, 1.0) self.assertEqual(sgdopt.miniBatchSize.defaultValue, 16) self.assertEqual(sgdopt.momentum.defaultValue, 0.0)
Example 21
Project: coremltools Author: apple File: test_keras2.py License: BSD 3-Clause "New" or "Revised" License | 5 votes |
def test_updatable_model_flag_cce_sgd_string(self): """ Tests the 'respect_trainable' flag when used along with string for the optimizer(keras internally creates an instance, here sgd), conversion is successful """ import coremltools from keras.layers import Dense, Input from keras.losses import categorical_crossentropy input = ["data"] output = ["output"] # This should result in an updatable model. inputs = Input(shape=(16,)) d1 = Dense(128)(inputs) d2 = Dense(10, name="foo", activation="softmax", trainable=True)(d1) kmodel = Model(inputs=inputs, outputs=d2) kmodel.compile( loss=categorical_crossentropy, optimizer="sgd", metrics=["accuracy"] ) cml = coremltools.converters.keras.convert( kmodel, input, output, respect_trainable=True ) spec = cml.get_spec() self.assertTrue(spec.isUpdatable) layers = spec.neuralNetwork.layers self.assertIsNotNone(layers[1].innerProduct) self.assertTrue(layers[1].innerProduct) self.assertTrue(layers[1].isUpdatable) self.assertEqual(len(spec.neuralNetwork.updateParams.lossLayers), 1) sgdopt = spec.neuralNetwork.updateParams.optimizer.sgdOptimizer # use almost equal for default verification with at least 5 decimal # places of closeness self.assertAlmostEqual(sgdopt.learningRate.defaultValue, 0.01, places=5) self.assertEqual(sgdopt.miniBatchSize.defaultValue, 16) self.assertEqual(sgdopt.momentum.defaultValue, 0.0)
Example 22
Project: coremltools Author: apple File: test_keras2.py License: BSD 3-Clause "New" or "Revised" License | 5 votes |
def test_updatable_model_flag_cce_adam_string(self): """ Tests the 'respect_trainable' flag when used along with string for the optimizer(keras internally creates an instance, here adam), conversion is successful """ import coremltools from keras.layers import Dense, Input from keras.losses import categorical_crossentropy input = ["data"] output = ["output"] # This should result in an updatable model. inputs = Input(shape=(16,)) d1 = Dense(128)(inputs) d2 = Dense(10, name="foo", activation="softmax", trainable=True)(d1) kmodel = Model(inputs=inputs, outputs=d2) kmodel.compile( loss=categorical_crossentropy, optimizer="adam", metrics=["accuracy"] ) cml = coremltools.converters.keras.convert( kmodel, input, output, respect_trainable=True ) spec = cml.get_spec() self.assertTrue(spec.isUpdatable) layers = spec.neuralNetwork.layers self.assertIsNotNone(layers[1].innerProduct) self.assertTrue(layers[1].innerProduct) self.assertTrue(layers[1].isUpdatable) self.assertEqual(len(spec.neuralNetwork.updateParams.lossLayers), 1) adopt = spec.neuralNetwork.updateParams.optimizer.adamOptimizer # use almost equal for default verification with at least 5 decimal # places of closeness self.assertAlmostEqual(adopt.learningRate.defaultValue, 0.001, places=5) self.assertAlmostEqual(adopt.miniBatchSize.defaultValue, 16) self.assertAlmostEqual(adopt.beta1.defaultValue, 0.90, places=5) self.assertAlmostEqual(adopt.beta2.defaultValue, 0.999, places=5)
Example 23
Project: DiscriminativeActiveLearning Author: dsgissin File: query_methods.py License: MIT License | 5 votes |
def compute_egls(self, unlabeled, n_classes): # create a function for computing the gradient length: self.input_placeholder = K.placeholder(self.model.get_input_shape_at(0)) self.output_placeholder = K.placeholder(self.model.get_output_shape_at(0)) predict = self.model.call(self.input_placeholder) loss = K.mean(categorical_crossentropy(self.output_placeholder, predict)) weights = [tensor for tensor in self.model.trainable_weights] gradient = self.model.optimizer.get_gradients(loss, weights) gradient_flat = [K.flatten(x) for x in gradient] gradient_flat = K.concatenate(gradient_flat) gradient_length = K.sum(K.square(gradient_flat)) self.get_gradient_length = K.function([K.learning_phase(), self.input_placeholder, self.output_placeholder], [gradient_length]) # calculate the expected gradient length of the unlabeled set (iteratively, to avoid memory issues): unlabeled_predictions = self.model.predict(unlabeled) egls = np.zeros(unlabeled.shape[0]) for i in range(n_classes): calculated_so_far = 0 while calculated_so_far < unlabeled_predictions.shape[0]: if calculated_so_far + 100 >= unlabeled_predictions.shape[0]: next = unlabeled_predictions.shape[0] - calculated_so_far else: next = 100 labels = np.zeros((next, n_classes)) labels[:,i] = 1 grads = self.get_gradient_length([0, unlabeled[calculated_so_far:calculated_so_far+next, :], labels])[0] grads *= unlabeled_predictions[calculated_so_far:calculated_so_far+next, i] egls[calculated_so_far:calculated_so_far+next] += grads calculated_so_far += next return egls
Example 24
Project: image-segmentation Author: nearthlab File: semantic_model_wrapper.py License: MIT License | 5 votes |
def cce_loss_graph(gt, pr): return K.mean(categorical_crossentropy(gt, pr))
Example 25
Project: DeepLearning_Wavelet-LSTM Author: hello-sea File: test_training.py License: MIT License | 5 votes |
def test_weighted_masked_objective(): a = Input(shape=(3,), name='input_a') # weighted_masked_objective def mask_dummy(y_true=None, y_pred=None, weight=None): return K.placeholder(y_true.shape) weighted_function = _weighted_masked_objective(losses.categorical_crossentropy) weighted_function(a, a, None)
Example 26
Project: DeepLearning_Wavelet-LSTM Author: hello-sea File: test_training.py License: MIT License | 5 votes |
def test_check_not_failing(): a = np.random.random((2, 1, 3)) _check_loss_and_target_compatibility([a], [losses.categorical_crossentropy], [a.shape]) _check_loss_and_target_compatibility([a], [losses.categorical_crossentropy], [(2, None, 3)])
Example 27
Project: DeepLearning_Wavelet-LSTM Author: hello-sea File: test_training.py License: MIT License | 5 votes |
def test_check_last_is_one(): a = np.random.random((2, 3, 1)) with pytest.raises(ValueError) as exc: _check_loss_and_target_compatibility([a], [losses.categorical_crossentropy], [a.shape]) assert 'You are passing a target array' in str(exc)
Example 28
Project: DeepLearning_Wavelet-LSTM Author: hello-sea File: test_training.py License: MIT License | 5 votes |
def test_check_bad_shape(): a = np.random.random((2, 3, 5)) with pytest.raises(ValueError) as exc: _check_loss_and_target_compatibility([a], [losses.categorical_crossentropy], [(2, 3, 6)]) assert 'targets to have the same shape' in str(exc)
Example 29
Project: DeepLearning_Wavelet-LSTM Author: hello-sea File: test_training.py License: MIT License | 5 votes |
def test_weighted_masked_objective(): a = Input(shape=(3,), name='input_a') # weighted_masked_objective def mask_dummy(y_true=None, y_pred=None, weight=None): return K.placeholder(y_true.shape) weighted_function = _weighted_masked_objective(losses.categorical_crossentropy) weighted_function(a, a, None)
Example 30
Project: DeepLearning_Wavelet-LSTM Author: hello-sea File: test_training.py License: MIT License | 5 votes |
def test_check_last_is_one(): a = np.random.random((2, 3, 1)) with pytest.raises(ValueError) as exc: _check_loss_and_target_compatibility([a], [losses.categorical_crossentropy], [a.shape]) assert 'You are passing a target array' in str(exc)