Python keras.losses.sparse_categorical_crossentropy() Examples

The following are 30 code examples of keras.losses.sparse_categorical_crossentropy(). 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. You may also want to check out all available functions/classes of the module keras.losses , or try the search function .
Example #1
Source File: models.py    From EEG_classification with Apache License 2.0 6 votes vote down vote up 
def get_model_lstm():
    nclass = 5

    seq_input = Input(shape=(None, 3000, 1))
    base_model = get_base_model()
    for layer in base_model.layers:
        layer.trainable = False
    encoded_sequence = TimeDistributed(base_model)(seq_input)
    encoded_sequence = Bidirectional(LSTM(100, return_sequences=True))(encoded_sequence)
    encoded_sequence = Dropout(rate=0.5)(encoded_sequence)
    encoded_sequence = Bidirectional(LSTM(100, return_sequences=True))(encoded_sequence)
    #out = TimeDistributed(Dense(nclass, activation="softmax"))(encoded_sequence)
    out = Convolution1D(nclass, kernel_size=1, activation="softmax", padding="same")(encoded_sequence)

    model = models.Model(seq_input, out)

    model.compile(optimizers.Adam(0.001), losses.sparse_categorical_crossentropy, metrics=['acc'])
    model.summary()

    return model
Example #2
Source File: losses_test.py    From DeepLearning_Wavelet-LSTM with MIT License 6 votes vote down vote up 
def test_sparse_categorical_crossentropy_4d():
    y_pred = K.variable(np.array([[[[0.7, 0.1, 0.2],
                                    [0.0, 0.3, 0.7],
                                    [0.1, 0.1, 0.8]],
                                   [[0.3, 0.7, 0.0],
                                    [0.3, 0.4, 0.3],
                                    [0.2, 0.5, 0.3]],
                                   [[0.8, 0.1, 0.1],
                                    [1.0, 0.0, 0.0],
                                    [0.4, 0.3, 0.3]]]]))
    y_true = K.variable(np.array([[[0, 1, 0],
                                   [2, 1, 0],
                                   [2, 2, 1]]]))
    expected_loss = - (np.log(0.7) + np.log(0.3) + np.log(0.1) +
                       np.log(K.epsilon()) + np.log(0.4) + np.log(0.2) +
                       np.log(0.1) + np.log(K.epsilon()) + np.log(0.3)) / 9
    loss = K.eval(losses.sparse_categorical_crossentropy(y_true, y_pred))
    assert np.isclose(expected_loss, np.mean(loss))
Example #3
Source File: train_audio.py    From AudioEmotion with MIT License 6 votes vote down vote up 
def train():
    model = create_model()
    model.compile(optimizer='adam',
                  loss=losses.sparse_categorical_crossentropy,
                  metrics=['accuracy'])
    checkpointer = callbacks.ModelCheckpoint(filepath="../Output/checkpoint.hdf5", verbose=1, save_best_only=True)
    x_train, x_test, y_train, y_test = load_audio_data()
    model.fit(x_train,
              y_train,
              epochs=1000,
              batch_size=1000,
              validation_split=0.2,
              callbacks=[checkpointer])
    results = model.evaluate(x_test, y_test)
    print('test_results: ', results)

    model.save(MODEL_FILE_PATH)
Example #4
Source File: losses_test.py    From DeepLearning_Wavelet-LSTM with MIT License 6 votes vote down vote up 
def test_sparse_categorical_crossentropy_4d():
    y_pred = K.variable(np.array([[[[0.7, 0.1, 0.2],
                                    [0.0, 0.3, 0.7],
                                    [0.1, 0.1, 0.8]],
                                   [[0.3, 0.7, 0.0],
                                    [0.3, 0.4, 0.3],
                                    [0.2, 0.5, 0.3]],
                                   [[0.8, 0.1, 0.1],
                                    [1.0, 0.0, 0.0],
                                    [0.4, 0.3, 0.3]]]]))
    y_true = K.variable(np.array([[[0, 1, 0],
                                   [2, 1, 0],
                                   [2, 2, 1]]]))
    expected_loss = - (np.log(0.7) + np.log(0.3) + np.log(0.1) +
                       np.log(K.epsilon()) + np.log(0.4) + np.log(0.2) +
                       np.log(0.1) + np.log(K.epsilon()) + np.log(0.3)) / 9
    loss = K.eval(losses.sparse_categorical_crossentropy(y_true, y_pred))
    assert np.isclose(expected_loss, np.mean(loss))
Example #5
Source File: losses_test.py    From DeepLearning_Wavelet-LSTM with MIT License 6 votes vote down vote up 
def test_sparse_categorical_crossentropy_4d():
    y_pred = K.variable(np.array([[[[0.7, 0.1, 0.2],
                                    [0.0, 0.3, 0.7],
                                    [0.1, 0.1, 0.8]],
                                   [[0.3, 0.7, 0.0],
                                    [0.3, 0.4, 0.3],
                                    [0.2, 0.5, 0.3]],
                                   [[0.8, 0.1, 0.1],
                                    [1.0, 0.0, 0.0],
                                    [0.4, 0.3, 0.3]]]]))
    y_true = K.variable(np.array([[[0, 1, 0],
                                   [2, 1, 0],
                                   [2, 2, 1]]]))
    expected_loss = - (np.log(0.7) + np.log(0.3) + np.log(0.1) +
                       np.log(K.epsilon()) + np.log(0.4) + np.log(0.2) +
                       np.log(0.1) + np.log(K.epsilon()) + np.log(0.3)) / 9
    loss = K.eval(losses.sparse_categorical_crossentropy(y_true, y_pred))
    assert np.isclose(expected_loss, np.mean(loss))
Example #6
Source File: losses_test.py    From DeepLearning_Wavelet-LSTM with MIT License 6 votes vote down vote up 
def test_sparse_categorical_crossentropy_4d():
    y_pred = K.variable(np.array([[[[0.7, 0.1, 0.2],
                                    [0.0, 0.3, 0.7],
                                    [0.1, 0.1, 0.8]],
                                   [[0.3, 0.7, 0.0],
                                    [0.3, 0.4, 0.3],
                                    [0.2, 0.5, 0.3]],
                                   [[0.8, 0.1, 0.1],
                                    [1.0, 0.0, 0.0],
                                    [0.4, 0.3, 0.3]]]]))
    y_true = K.variable(np.array([[[0, 1, 0],
                                   [2, 1, 0],
                                   [2, 2, 1]]]))
    expected_loss = - (np.log(0.7) + np.log(0.3) + np.log(0.1) +
                       np.log(K.epsilon()) + np.log(0.4) + np.log(0.2) +
                       np.log(0.1) + np.log(K.epsilon()) + np.log(0.3)) / 9
    loss = K.eval(losses.sparse_categorical_crossentropy(y_true, y_pred))
    assert np.isclose(expected_loss, np.mean(loss))
Example #7
Source File: keras_bert_layer.py    From nlp_xiaojiang with MIT License 6 votes vote down vote up 
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 #8
Source File: crf_losses.py    From keras-contrib with MIT License 6 votes vote down vote up 
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 #9
Source File: losses_test.py    From DeepLearning_Wavelet-LSTM with MIT License 5 votes vote down vote up 
def test_sparse_categorical_crossentropy():
    y_pred = K.variable(np.array([[0.3, 0.6, 0.1],
                                  [0.1, 0.2, 0.7]]))
    y_true = K.variable(np.array([1, 2]))
    expected_loss = - (np.log(0.6) + np.log(0.7)) / 2
    loss = K.eval(losses.sparse_categorical_crossentropy(y_true, y_pred))
    assert np.isclose(expected_loss, np.mean(loss))
Example #10
Source File: models.py    From EEG_classification with Apache License 2.0 5 votes vote down vote up 
def get_model():
    nclass = 5
    inp = Input(shape=(3000, 1))
    img_1 = Convolution1D(16, kernel_size=5, activation=activations.relu, padding="valid")(inp)
    img_1 = Convolution1D(16, kernel_size=5, activation=activations.relu, padding="valid")(img_1)
    img_1 = MaxPool1D(pool_size=2)(img_1)
    img_1 = SpatialDropout1D(rate=0.01)(img_1)
    img_1 = Convolution1D(32, kernel_size=3, activation=activations.relu, padding="valid")(img_1)
    img_1 = Convolution1D(32, kernel_size=3, activation=activations.relu, padding="valid")(img_1)
    img_1 = MaxPool1D(pool_size=2)(img_1)
    img_1 = SpatialDropout1D(rate=0.01)(img_1)
    img_1 = Convolution1D(32, kernel_size=3, activation=activations.relu, padding="valid")(img_1)
    img_1 = Convolution1D(32, kernel_size=3, activation=activations.relu, padding="valid")(img_1)
    img_1 = MaxPool1D(pool_size=2)(img_1)
    img_1 = SpatialDropout1D(rate=0.01)(img_1)
    img_1 = Convolution1D(256, kernel_size=3, activation=activations.relu, padding="valid")(img_1)
    img_1 = Convolution1D(256, kernel_size=3, activation=activations.relu, padding="valid")(img_1)
    img_1 = GlobalMaxPool1D()(img_1)
    img_1 = Dropout(rate=0.01)(img_1)

    dense_1 = Dropout(rate=0.01)(Dense(64, activation=activations.relu, name="dense_1")(img_1))
    dense_1 = Dropout(rate=0.05)(Dense(64, activation=activations.relu, name="dense_2")(dense_1))
    dense_1 = Dense(nclass, activation=activations.softmax, name="dense_3")(dense_1)

    model = models.Model(inputs=inp, outputs=dense_1)
    opt = optimizers.Adam(0.001)

    model.compile(optimizer=opt, loss=losses.sparse_categorical_crossentropy, metrics=['acc'])
    model.summary()
    return model
Example #11
Source File: losses_test.py    From DeepLearning_Wavelet-LSTM with MIT License 5 votes vote down vote up 
def test_cce_one_hot():
    y_a = K.variable(np.random.randint(0, 7, (5, 6)))
    y_b = K.variable(np.random.random((5, 6, 7)))
    objective_output = losses.sparse_categorical_crossentropy(y_a, y_b)
    assert K.eval(objective_output).shape == (5, 6)

    y_a = K.variable(np.random.randint(0, 7, (6,)))
    y_b = K.variable(np.random.random((6, 7)))
    assert K.eval(losses.sparse_categorical_crossentropy(y_a, y_b)).shape == (6,)
Example #12
Source File: models.py    From EEG_classification with Apache License 2.0 5 votes vote down vote up 
def get_base_model():
    inp = Input(shape=(3000, 1))
    img_1 = Convolution1D(16, kernel_size=5, activation=activations.relu, padding="valid")(inp)
    img_1 = Convolution1D(16, kernel_size=5, activation=activations.relu, padding="valid")(img_1)
    img_1 = MaxPool1D(pool_size=2)(img_1)
    img_1 = SpatialDropout1D(rate=0.01)(img_1)
    img_1 = Convolution1D(32, kernel_size=3, activation=activations.relu, padding="valid")(img_1)
    img_1 = Convolution1D(32, kernel_size=3, activation=activations.relu, padding="valid")(img_1)
    img_1 = MaxPool1D(pool_size=2)(img_1)
    img_1 = SpatialDropout1D(rate=0.01)(img_1)
    img_1 = Convolution1D(32, kernel_size=3, activation=activations.relu, padding="valid")(img_1)
    img_1 = Convolution1D(32, kernel_size=3, activation=activations.relu, padding="valid")(img_1)
    img_1 = MaxPool1D(pool_size=2)(img_1)
    img_1 = SpatialDropout1D(rate=0.01)(img_1)
    img_1 = Convolution1D(256, kernel_size=3, activation=activations.relu, padding="valid")(img_1)
    img_1 = Convolution1D(256, kernel_size=3, activation=activations.relu, padding="valid")(img_1)
    img_1 = GlobalMaxPool1D()(img_1)
    img_1 = Dropout(rate=0.01)(img_1)

    dense_1 = Dropout(0.01)(Dense(64, activation=activations.relu, name="dense_1")(img_1))

    base_model = models.Model(inputs=inp, outputs=dense_1)
    opt = optimizers.Adam(0.001)

    base_model.compile(optimizer=opt, loss=losses.sparse_categorical_crossentropy, metrics=['acc'])
    #model.summary()
    return base_model
Example #13
Source File: losses_test.py    From DeepLearning_Wavelet-LSTM with MIT License 5 votes vote down vote up 
def test_sparse_categorical_crossentropy():
    y_pred = K.variable(np.array([[0.3, 0.6, 0.1],
                                  [0.1, 0.2, 0.7]]))
    y_true = K.variable(np.array([1, 2]))
    expected_loss = - (np.log(0.6) + np.log(0.7)) / 2
    loss = K.eval(losses.sparse_categorical_crossentropy(y_true, y_pred))
    assert np.isclose(expected_loss, np.mean(loss))
Example #14
Source File: losses_test.py    From DeepLearning_Wavelet-LSTM with MIT License 5 votes vote down vote up 
def test_cce_one_hot():
    y_a = K.variable(np.random.randint(0, 7, (5, 6)))
    y_b = K.variable(np.random.random((5, 6, 7)))
    objective_output = losses.sparse_categorical_crossentropy(y_a, y_b)
    assert K.eval(objective_output).shape == (5, 6)

    y_a = K.variable(np.random.randint(0, 7, (6,)))
    y_b = K.variable(np.random.random((6, 7)))
    assert K.eval(losses.sparse_categorical_crossentropy(y_a, y_b)).shape == (6,)
Example #15
Source File: losses_test.py    From DeepLearning_Wavelet-LSTM with MIT License 5 votes vote down vote up 
def test_sparse_categorical_crossentropy():
    y_pred = K.variable(np.array([[0.3, 0.6, 0.1],
                                  [0.1, 0.2, 0.7]]))
    y_true = K.variable(np.array([1, 2]))
    expected_loss = - (np.log(0.6) + np.log(0.7)) / 2
    loss = K.eval(losses.sparse_categorical_crossentropy(y_true, y_pred))
    assert np.isclose(expected_loss, np.mean(loss))
Example #16
Source File: models.py    From EEG_classification with Apache License 2.0 5 votes vote down vote up 
def get_model_cnn():
    nclass = 5

    seq_input = Input(shape=(None, 3000, 1))
    base_model = get_base_model()
    # for layer in base_model.layers:
    #     layer.trainable = False
    encoded_sequence = TimeDistributed(base_model)(seq_input)
    encoded_sequence = SpatialDropout1D(rate=0.01)(Convolution1D(128,
                                                               kernel_size=3,
                                                               activation="relu",
                                                               padding="same")(encoded_sequence))
    encoded_sequence = Dropout(rate=0.05)(Convolution1D(128,
                                                               kernel_size=3,
                                                               activation="relu",
                                                               padding="same")(encoded_sequence))

    #out = TimeDistributed(Dense(nclass, activation="softmax"))(encoded_sequence)
    out = Convolution1D(nclass, kernel_size=3, activation="softmax", padding="same")(encoded_sequence)

    model = models.Model(seq_input, out)

    model.compile(optimizers.Adam(0.001), losses.sparse_categorical_crossentropy, metrics=['acc'])
    model.summary()

    return model
Example #17
Source File: losses_test.py    From DeepLearning_Wavelet-LSTM with MIT License 5 votes vote down vote up 
def test_cce_one_hot():
    y_a = K.variable(np.random.randint(0, 7, (5, 6)))
    y_b = K.variable(np.random.random((5, 6, 7)))
    objective_output = losses.sparse_categorical_crossentropy(y_a, y_b)
    assert K.eval(objective_output).shape == (5, 6)

    y_a = K.variable(np.random.randint(0, 7, (6,)))
    y_b = K.variable(np.random.random((6, 7)))
    assert K.eval(losses.sparse_categorical_crossentropy(y_a, y_b)).shape == (6,)
Example #18
Source File: losses_test.py    From DeepLearning_Wavelet-LSTM with MIT License 5 votes vote down vote up 
def test_sparse_categorical_crossentropy():
    y_pred = K.variable(np.array([[0.3, 0.6, 0.1],
                                  [0.1, 0.2, 0.7]]))
    y_true = K.variable(np.array([1, 2]))
    expected_loss = - (np.log(0.6) + np.log(0.7)) / 2
    loss = K.eval(losses.sparse_categorical_crossentropy(y_true, y_pred))
    assert np.isclose(expected_loss, np.mean(loss))
Example #19
Source File: losses_test.py    From DeepLearning_Wavelet-LSTM with MIT License 5 votes vote down vote up 
def test_sparse_categorical_crossentropy():
    y_pred = K.variable(np.array([[0.3, 0.6, 0.1],
                                  [0.1, 0.2, 0.7]]))
    y_true = K.variable(np.array([1, 2]))
    expected_loss = - (np.log(0.6) + np.log(0.7)) / 2
    loss = K.eval(losses.sparse_categorical_crossentropy(y_true, y_pred))
    assert np.isclose(expected_loss, np.mean(loss))
Example #20
Source File: losses_test.py    From DeepLearning_Wavelet-LSTM with MIT License 5 votes vote down vote up 
def test_cce_one_hot():
    y_a = K.variable(np.random.randint(0, 7, (5, 6)))
    y_b = K.variable(np.random.random((5, 6, 7)))
    objective_output = losses.sparse_categorical_crossentropy(y_a, y_b)
    assert K.eval(objective_output).shape == (5, 6)

    y_a = K.variable(np.random.randint(0, 7, (6,)))
    y_b = K.variable(np.random.random((6, 7)))
    assert K.eval(losses.sparse_categorical_crossentropy(y_a, y_b)).shape == (6,)
Example #21
Source File: losses_test.py    From DeepLearning_Wavelet-LSTM with MIT License 5 votes vote down vote up 
def test_cce_one_hot():
    y_a = K.variable(np.random.randint(0, 7, (5, 6)))
    y_b = K.variable(np.random.random((5, 6, 7)))
    objective_output = losses.sparse_categorical_crossentropy(y_a, y_b)
    assert K.eval(objective_output).shape == (5, 6)

    y_a = K.variable(np.random.randint(0, 7, (6,)))
    y_b = K.variable(np.random.random((6, 7)))
    assert K.eval(losses.sparse_categorical_crossentropy(y_a, y_b)).shape == (6,)
Example #22
Source File: losses_test.py    From DeepLearning_Wavelet-LSTM with MIT License 5 votes vote down vote up 
def test_sparse_categorical_crossentropy():
    y_pred = K.variable(np.array([[0.3, 0.6, 0.1],
                                  [0.1, 0.2, 0.7]]))
    y_true = K.variable(np.array([1, 2]))
    expected_loss = - (np.log(0.6) + np.log(0.7)) / 2
    loss = K.eval(losses.sparse_categorical_crossentropy(y_true, y_pred))
    assert np.isclose(expected_loss, np.mean(loss))
Example #23
Source File: losses_test.py    From DeepLearning_Wavelet-LSTM with MIT License 5 votes vote down vote up 
def test_cce_one_hot():
    y_a = K.variable(np.random.randint(0, 7, (5, 6)))
    y_b = K.variable(np.random.random((5, 6, 7)))
    objective_output = losses.sparse_categorical_crossentropy(y_a, y_b)
    assert K.eval(objective_output).shape == (5, 6)

    y_a = K.variable(np.random.randint(0, 7, (6,)))
    y_b = K.variable(np.random.random((6, 7)))
    assert K.eval(losses.sparse_categorical_crossentropy(y_a, y_b)).shape == (6,)
Example #24
Source File: losses_test.py    From DeepLearning_Wavelet-LSTM with MIT License 5 votes vote down vote up 
def test_sparse_categorical_crossentropy():
    y_pred = K.variable(np.array([[0.3, 0.6, 0.1],
                                  [0.1, 0.2, 0.7]]))
    y_true = K.variable(np.array([1, 2]))
    expected_loss = - (np.log(0.6) + np.log(0.7)) / 2
    loss = K.eval(losses.sparse_categorical_crossentropy(y_true, y_pred))
    assert np.isclose(expected_loss, np.mean(loss))
Example #25
Source File: losses_test.py    From DeepLearning_Wavelet-LSTM with MIT License 5 votes vote down vote up 
def test_cce_one_hot():
    y_a = K.variable(np.random.randint(0, 7, (5, 6)))
    y_b = K.variable(np.random.random((5, 6, 7)))
    objective_output = losses.sparse_categorical_crossentropy(y_a, y_b)
    assert K.eval(objective_output).shape == (5, 6)

    y_a = K.variable(np.random.randint(0, 7, (6,)))
    y_b = K.variable(np.random.random((6, 7)))
    assert K.eval(losses.sparse_categorical_crossentropy(y_a, y_b)).shape == (6,)
Example #26
Source File: losses_test.py    From DeepLearning_Wavelet-LSTM with MIT License 5 votes vote down vote up 
def test_sparse_categorical_crossentropy():
    y_pred = K.variable(np.array([[0.3, 0.6, 0.1],
                                  [0.1, 0.2, 0.7]]))
    y_true = K.variable(np.array([1, 2]))
    expected_loss = - (np.log(0.6) + np.log(0.7)) / 2
    loss = K.eval(losses.sparse_categorical_crossentropy(y_true, y_pred))
    assert np.isclose(expected_loss, np.mean(loss))
Example #27
Source File: losses_test.py    From DeepLearning_Wavelet-LSTM with MIT License 5 votes vote down vote up 
def test_cce_one_hot():
    y_a = K.variable(np.random.randint(0, 7, (5, 6)))
    y_b = K.variable(np.random.random((5, 6, 7)))
    objective_output = losses.sparse_categorical_crossentropy(y_a, y_b)
    assert K.eval(objective_output).shape == (5, 6)

    y_a = K.variable(np.random.randint(0, 7, (6,)))
    y_b = K.variable(np.random.random((6, 7)))
    assert K.eval(losses.sparse_categorical_crossentropy(y_a, y_b)).shape == (6,)
Example #28
Source File: baseline_mitbih.py    From ECG_Heartbeat_Classification with MIT License 5 votes vote down vote up 
def get_model():
    nclass = 5
    inp = Input(shape=(187, 1))
    img_1 = Convolution1D(16, kernel_size=5, activation=activations.relu, padding="valid")(inp)
    img_1 = Convolution1D(16, kernel_size=5, activation=activations.relu, padding="valid")(img_1)
    img_1 = MaxPool1D(pool_size=2)(img_1)
    img_1 = Dropout(rate=0.1)(img_1)
    img_1 = Convolution1D(32, kernel_size=3, activation=activations.relu, padding="valid")(img_1)
    img_1 = Convolution1D(32, kernel_size=3, activation=activations.relu, padding="valid")(img_1)
    img_1 = MaxPool1D(pool_size=2)(img_1)
    img_1 = Dropout(rate=0.1)(img_1)
    img_1 = Convolution1D(32, kernel_size=3, activation=activations.relu, padding="valid")(img_1)
    img_1 = Convolution1D(32, kernel_size=3, activation=activations.relu, padding="valid")(img_1)
    img_1 = MaxPool1D(pool_size=2)(img_1)
    img_1 = Dropout(rate=0.1)(img_1)
    img_1 = Convolution1D(256, kernel_size=3, activation=activations.relu, padding="valid")(img_1)
    img_1 = Convolution1D(256, kernel_size=3, activation=activations.relu, padding="valid")(img_1)
    img_1 = GlobalMaxPool1D()(img_1)
    img_1 = Dropout(rate=0.2)(img_1)

    dense_1 = Dense(64, activation=activations.relu, name="dense_1")(img_1)
    dense_1 = Dense(64, activation=activations.relu, name="dense_2")(dense_1)
    dense_1 = Dense(nclass, activation=activations.softmax, name="dense_3_mitbih")(dense_1)

    model = models.Model(inputs=inp, outputs=dense_1)
    opt = optimizers.Adam(0.001)

    model.compile(optimizer=opt, loss=losses.sparse_categorical_crossentropy, metrics=['acc'])
    model.summary()
    return model
Example #29
Source File: dssim_test.py    From keras-contrib with MIT License 5 votes vote down vote up 
def test_cce_one_hot():
    y_a = K.variable(np.random.randint(0, 7, (5, 6)))
    y_b = K.variable(np.random.random((5, 6, 7)))
    objective_output = sparse_categorical_crossentropy(y_a, y_b)
    assert K.eval(objective_output).shape == (5, 6)

    y_a = K.variable(np.random.randint(0, 7, (6,)))
    y_b = K.variable(np.random.random((6, 7)))
    assert K.eval(sparse_categorical_crossentropy(y_a, y_b)).shape == (6,)
Example #30
Source File: Categorical.py    From keras-pandas with MIT License 5 votes vote down vote up 
def output_suggested_loss(self):
        self._check_output_support()
        suggested_loss = losses.sparse_categorical_crossentropy
        return suggested_loss