Python Sequential.evaluate方法代码示例

# 需要导入模块: from keras.models import Sequential [as 别名]
# 或者: from keras.models.Sequential import evaluate [as 别名]
def TrainAndValidation1(X_train,y_train,X_test,y_test,bEarlyStopByTestData=True):    
    
    print "#\tTraining shape:" , X_train.shape
    print "#\tTraining label:" , y_train.shape   
    #============================================
    # Model preparation
    #============================================
    model = Sequential()
    model.add(Dense(output_dim=64,input_dim=X_train.shape[1], init='uniform'))
    model.add(Activation(DEEP_AVF))

    model.add(Dense(64, init='uniform'))
    model.add(Activation(DEEP_AVF))

    model.add(Dense(sparkcore.WORKING_KLABEL, init='uniform'))
    model.add(Activation('softmax'))
    sgd = SGD(lr=DEEP_SGDLR, decay=1e-6, momentum=0.9, nesterov=True)
    model.compile(loss=DEEP_LOSSFUNC, optimizer=sgd)
    
    a = model.fit(X_train, y_train,nb_epoch=5)
    score0 = model.evaluate(X_train, y_train, batch_size=DEEP_BSIZE)

    if not X_test is None:
        score1  = model.evaluate(X_test, y_test, batch_size=DEEP_BSIZE)
        predicted = model.predict(X_test)
        v_precision,v_recall,TP, FP, TN, FN,thelogloss = sparkcore.MyEvaluation(y_test,predicted)
        return score0,score1,v_precision,v_recall,TP, FP, TN, FN, thelogloss,model
    else:
        return score0, 0,0,0,0,0,0,0,0,model        

# 需要导入模块: from keras.models import Sequential [as 别名]
# 或者: from keras.models.Sequential import evaluate [as 别名]
    def test_conv2d(self):
        # Generate dummy data
        x_train = np.random.random((100, 100, 100, 3))
        y_train = keras.utils.to_categorical(np.random.randint(10, size=(100, 1)), num_classes=10)
        x_test = np.random.random((20, 100, 100, 3))
        y_test = keras.utils.to_categorical(np.random.randint(10, size=(20, 1)), num_classes=10)

        model = Sequential()
        # input: 100x100 images with 3 channels -> (100, 100, 3) tensors.
        # this applies 32 convolution filters of size 3x3 each.
        model.add(Conv2D(32, (3, 3), activation='relu', input_shape=(100, 100, 3)))
        model.add(Conv2D(32, (3, 3), activation='relu'))
        model.add(MaxPooling2D(pool_size=(2, 2)))
        model.add(Dropout(0.25))

        model.add(Conv2D(64, (3, 3), activation='relu'))
        model.add(Conv2D(64, (3, 3), activation='relu'))
        model.add(MaxPooling2D(pool_size=(2, 2)))
        model.add(Dropout(0.25))

        model.add(Flatten())
        model.add(Dense(256, activation='relu'))
        model.add(Dropout(0.5))
        model.add(Dense(10, activation='softmax'))

        sgd = SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True)

        # This throws if libcudnn is not properly installed with on a GPU
        model.compile(loss='categorical_crossentropy', optimizer=sgd)
        model.fit(x_train, y_train, batch_size=32, epochs=1)
        model.evaluate(x_test, y_test, batch_size=32)

# 需要导入模块: from keras.models import Sequential [as 别名]
# 或者: from keras.models.Sequential import evaluate [as 别名]
def test_nested_sequential():
    (X_train, y_train), (X_test, y_test) = _get_test_data()

    inner = Sequential()
    inner.add(Dense(nb_hidden, input_shape=(input_dim,)))
    inner.add(Activation("relu"))
    inner.add(Dense(nb_class))

    middle = Sequential()
    middle.add(inner)

    model = Sequential()
    model.add(middle)
    model.add(Activation("softmax"))
    model.compile(loss="categorical_crossentropy", optimizer="rmsprop")

    model.fit(X_train, y_train, batch_size=batch_size, nb_epoch=nb_epoch, verbose=1, validation_data=(X_test, y_test))
    model.fit(X_train, y_train, batch_size=batch_size, nb_epoch=nb_epoch, verbose=2, validation_split=0.1)
    model.fit(X_train, y_train, batch_size=batch_size, nb_epoch=nb_epoch, verbose=0)
    model.fit(X_train, y_train, batch_size=batch_size, nb_epoch=nb_epoch, verbose=1, shuffle=False)

    model.train_on_batch(X_train[:32], y_train[:32])

    loss = model.evaluate(X_test, y_test, verbose=0)

    model.predict(X_test, verbose=0)
    model.predict_classes(X_test, verbose=0)
    model.predict_proba(X_test, verbose=0)

    fname = "test_nested_sequential_temp.h5"
    model.save_weights(fname, overwrite=True)

    inner = Sequential()
    inner.add(Dense(nb_hidden, input_shape=(input_dim,)))
    inner.add(Activation("relu"))
    inner.add(Dense(nb_class))

    middle = Sequential()
    middle.add(inner)

    model = Sequential()
    model.add(middle)
    model.add(Activation("softmax"))
    model.compile(loss="categorical_crossentropy", optimizer="rmsprop")
    model.load_weights(fname)
    os.remove(fname)

    nloss = model.evaluate(X_test, y_test, verbose=0)
    assert loss == nloss

    # test serialization
    config = model.get_config()
    new_model = Sequential.from_config(config)

    model.summary()
    json_str = model.to_json()
    new_model = model_from_json(json_str)

    yaml_str = model.to_yaml()
    new_model = model_from_yaml(yaml_str)

# 需要导入模块: from keras.models import Sequential [as 别名]
# 或者: from keras.models.Sequential import evaluate [as 别名]
    def test_recursive(self):
        print('test layer-like API')

        graph = containers.Graph()
        graph.add_input(name='input1', ndim=2)
        graph.add_node(Dense(32, 16), name='dense1', input='input1')
        graph.add_node(Dense(32, 4), name='dense2', input='input1')
        graph.add_node(Dense(16, 4), name='dense3', input='dense1')
        graph.add_output(name='output1', inputs=['dense2', 'dense3'], merge_mode='sum')

        seq = Sequential()
        seq.add(Dense(32, 32, name='first_seq_dense'))
        seq.add(graph)
        seq.add(Dense(4, 4, name='last_seq_dense'))

        seq.compile('rmsprop', 'mse')

        history = seq.fit(X_train, y_train, batch_size=10, nb_epoch=10)
        loss = seq.evaluate(X_test, y_test)
        print(loss)
        assert(loss < 1.4)

        loss = seq.evaluate(X_test, y_test, show_accuracy=True)
        pred = seq.predict(X_test)
        seq.get_config(verbose=1)

# 需要导入模块: from keras.models import Sequential [as 别名]
# 或者: from keras.models.Sequential import evaluate [as 别名]
def test_sequential_fit_generator():
    (x_train, y_train), (x_test, y_test) = _get_test_data()

    def data_generator(train):
        if train:
            max_batch_index = len(x_train) // batch_size
        else:
            max_batch_index = len(x_test) // batch_size
        i = 0
        while 1:
            if train:
                yield (x_train[i * batch_size: (i + 1) * batch_size], y_train[i * batch_size: (i + 1) * batch_size])
            else:
                yield (x_test[i * batch_size: (i + 1) * batch_size], y_test[i * batch_size: (i + 1) * batch_size])
            i += 1
            i = i % max_batch_index

    model = Sequential()
    model.add(Dense(num_hidden, input_shape=(input_dim,)))
    model.add(Activation('relu'))
    model.add(Dense(num_class))
    model.pop()
    model.add(Dense(num_class))
    model.add(Activation('softmax'))
    model.compile(loss='categorical_crossentropy', optimizer='rmsprop')

    model.fit_generator(data_generator(True), 5, epochs)
    model.fit_generator(data_generator(True), 5, epochs,
                        validation_data=(x_test, y_test))
    model.fit_generator(data_generator(True), 5, epochs,
                        validation_data=data_generator(False),
                        validation_steps=3)
    model.fit_generator(data_generator(True), 5, epochs, max_queue_size=2)
    model.evaluate(x_train, y_train)

# 需要导入模块: from keras.models import Sequential [as 别名]
# 或者: from keras.models.Sequential import evaluate [as 别名]
def test_recursive():
    # test layer-like API

    graph = containers.Graph()
    graph.add_input(name='input1', input_shape=(32,))
    graph.add_node(Dense(16), name='dense1', input='input1')
    graph.add_node(Dense(4), name='dense2', input='input1')
    graph.add_node(Dense(4), name='dense3', input='dense1')
    graph.add_output(name='output1', inputs=['dense2', 'dense3'],
                     merge_mode='sum')

    seq = Sequential()
    seq.add(Dense(32, input_shape=(32,)))
    seq.add(graph)
    seq.add(Dense(4))

    seq.compile('rmsprop', 'mse')

    seq.fit(X_train_graph, y_train_graph, batch_size=10, nb_epoch=10)
    loss = seq.evaluate(X_test_graph, y_test_graph)
    assert(loss < 2.5)

    loss = seq.evaluate(X_test_graph, y_test_graph, show_accuracy=True)
    seq.predict(X_test_graph)
    seq.get_config(verbose=1)

# 需要导入模块: from keras.models import Sequential [as 别名]
# 或者: from keras.models.Sequential import evaluate [as 别名]
    def test_recursive(self):
        print("test layer-like API")

        graph = containers.Graph()
        graph.add_input(name="input1", ndim=2)
        graph.add_node(Dense(32, 16), name="dense1", input="input1")
        graph.add_node(Dense(32, 4), name="dense2", input="input1")
        graph.add_node(Dense(16, 4), name="dense3", input="dense1")
        graph.add_output(name="output1", inputs=["dense2", "dense3"], merge_mode="sum")

        seq = Sequential()
        seq.add(Dense(32, 32, name="first_seq_dense"))
        seq.add(graph)
        seq.add(Dense(4, 4, name="last_seq_dense"))

        seq.compile("rmsprop", "mse")

        history = seq.fit(X_train, y_train, batch_size=10, nb_epoch=10)
        loss = seq.evaluate(X_test, y_test)
        print(loss)
        assert loss < 2.5

        loss = seq.evaluate(X_test, y_test, show_accuracy=True)
        pred = seq.predict(X_test)
        seq.get_config(verbose=1)

# 需要导入模块: from keras.models import Sequential [as 别名]
# 或者: from keras.models.Sequential import evaluate [as 别名]
def test_nested_sequential(in_tmpdir):
    (x_train, y_train), (x_test, y_test) = _get_test_data()

    inner = Sequential()
    inner.add(Dense(num_hidden, input_shape=(input_dim,)))
    inner.add(Activation('relu'))
    inner.add(Dense(num_class))

    middle = Sequential()
    middle.add(inner)

    model = Sequential()
    model.add(middle)
    model.add(Activation('softmax'))
    model.compile(loss='categorical_crossentropy', optimizer='rmsprop')

    model.fit(x_train, y_train, batch_size=batch_size, epochs=epochs, verbose=1, validation_data=(x_test, y_test))
    model.fit(x_train, y_train, batch_size=batch_size, epochs=epochs, verbose=2, validation_split=0.1)
    model.fit(x_train, y_train, batch_size=batch_size, epochs=epochs, verbose=0)
    model.fit(x_train, y_train, batch_size=batch_size, epochs=epochs, verbose=1, shuffle=False)

    model.train_on_batch(x_train[:32], y_train[:32])

    loss = model.evaluate(x_test, y_test, verbose=0)

    model.predict(x_test, verbose=0)
    model.predict_classes(x_test, verbose=0)
    model.predict_proba(x_test, verbose=0)

    fname = 'test_nested_sequential_temp.h5'
    model.save_weights(fname, overwrite=True)

    inner = Sequential()
    inner.add(Dense(num_hidden, input_shape=(input_dim,)))
    inner.add(Activation('relu'))
    inner.add(Dense(num_class))

    middle = Sequential()
    middle.add(inner)

    model = Sequential()
    model.add(middle)
    model.add(Activation('softmax'))
    model.compile(loss='categorical_crossentropy', optimizer='rmsprop')
    model.load_weights(fname)
    os.remove(fname)

    nloss = model.evaluate(x_test, y_test, verbose=0)
    assert(loss == nloss)

    # test serialization
    config = model.get_config()
    Sequential.from_config(config)

    model.summary()
    json_str = model.to_json()
    model_from_json(json_str)

    yaml_str = model.to_yaml()
    model_from_yaml(yaml_str)

# 需要导入模块: from keras.models import Sequential [as 别名]
# 或者: from keras.models.Sequential import evaluate [as 别名]
def mlp_model(X_train, y_train, X_test, y_test):
    tokenizer = Tokenizer(nb_words=1000)
    nb_classes = np.max(y_train) + 1

    X_train = tokenizer.sequences_to_matrix(X_train, mode="freq")
    X_test = tokenizer.sequences_to_matrix(X_test, mode="freq")

    Y_train = np_utils.to_categorical(y_train, nb_classes)
    Y_test = np_utils.to_categorical(y_test, nb_classes)

    print("Building model...")
    model = Sequential()
    model.add(Dense(512, input_shape=(max_len,)))
    model.add(Activation('relu'))
    model.add(Dropout(0.5))
    model.add(Dense(nb_classes))
    model.add(Activation('softmax'))

    model.compile(loss='categorical_crossentropy', optimizer='adam', class_mode='categorical')

    history = model.fit(X_train, Y_train, nb_epoch=nb_epoch, batch_size=batch_size, verbose=1, show_accuracy=True, validation_split=0.1)
    model.evaluate(X_test, Y_test, batch_size=batch_size, verbose=1, show_accuracy=True)
    # print('Test score:', score[0])
    # print('Test accuracy:', score[1])
    pred_labels = model.predict_classes(X_test)
    # print pred_labels
    # print y_test
    accuracy = accuracy_score(y_test, pred_labels)
    precision, recall, f1, supp = precision_recall_fscore_support(y_test, pred_labels, average='weighted')
    print precision, recall, f1, supp

    return accuracy, precision, recall, f1

# 需要导入模块: from keras.models import Sequential [as 别名]
# 或者: from keras.models.Sequential import evaluate [as 别名]
def test_merge_overlap():
    left = Sequential()
    left.add(Dense(nb_hidden, input_shape=(input_dim,)))
    left.add(Activation('relu'))

    model = Sequential()
    model.add(Merge([left, left], mode='sum'))
    model.add(Dense(nb_class))
    model.add(Activation('softmax'))
    model.compile(loss='categorical_crossentropy', optimizer='rmsprop')

    model.fit(X_train, y_train, batch_size=batch_size, nb_epoch=nb_epoch, show_accuracy=True, verbose=1, validation_data=(X_test, y_test))
    model.fit(X_train, y_train, batch_size=batch_size, nb_epoch=nb_epoch, show_accuracy=False, verbose=2, validation_data=(X_test, y_test))
    model.fit(X_train, y_train, batch_size=batch_size, nb_epoch=nb_epoch, show_accuracy=True, verbose=2, validation_split=0.1)
    model.fit(X_train, y_train, batch_size=batch_size, nb_epoch=nb_epoch, show_accuracy=False, verbose=1, validation_split=0.1)
    model.fit(X_train, y_train, batch_size=batch_size, nb_epoch=nb_epoch, verbose=0)
    model.fit(X_train, y_train, batch_size=batch_size, nb_epoch=nb_epoch, verbose=1, shuffle=False)

    model.train_on_batch(X_train[:32], y_train[:32])

    loss = model.evaluate(X_train, y_train, verbose=0)
    assert(loss < 0.7)
    model.predict(X_test, verbose=0)
    model.predict_classes(X_test, verbose=0)
    model.predict_proba(X_test, verbose=0)
    model.get_config(verbose=0)

    fname = 'test_merge_overlap_temp.h5'
    model.save_weights(fname, overwrite=True)
    model.load_weights(fname)
    os.remove(fname)

    nloss = model.evaluate(X_train, y_train, verbose=0)
    assert(loss == nloss)

# 需要导入模块: from keras.models import Sequential [as 别名]
# 或者: from keras.models.Sequential import evaluate [as 别名]
def test_merge_sum():
    (X_train, y_train), (X_test, y_test) = _get_test_data()
    left = Sequential()
    left.add(Dense(nb_hidden, input_shape=(input_dim,)))
    left.add(Activation('relu'))

    right = Sequential()
    right.add(Dense(nb_hidden, input_shape=(input_dim,)))
    right.add(Activation('relu'))

    model = Sequential()
    model.add(Merge([left, right], mode='sum'))
    model.add(Dense(nb_class))
    model.add(Activation('softmax'))
    model.compile(loss='categorical_crossentropy', optimizer='rmsprop')

    model.fit([X_train, X_train], y_train, batch_size=batch_size, nb_epoch=nb_epoch, verbose=0, validation_data=([X_test, X_test], y_test))
    model.fit([X_train, X_train], y_train, batch_size=batch_size, nb_epoch=nb_epoch, verbose=0, validation_split=0.1)
    model.fit([X_train, X_train], y_train, batch_size=batch_size, nb_epoch=nb_epoch, verbose=0)
    model.fit([X_train, X_train], y_train, batch_size=batch_size, nb_epoch=nb_epoch, verbose=0, shuffle=False)

    loss = model.evaluate([X_test, X_test], y_test, verbose=0)

    model.predict([X_test, X_test], verbose=0)
    model.predict_classes([X_test, X_test], verbose=0)
    model.predict_proba([X_test, X_test], verbose=0)

    # test weight saving
    fname = 'test_merge_sum_temp.h5'
    model.save_weights(fname, overwrite=True)
    left = Sequential()
    left.add(Dense(nb_hidden, input_shape=(input_dim,)))
    left.add(Activation('relu'))
    right = Sequential()
    right.add(Dense(nb_hidden, input_shape=(input_dim,)))
    right.add(Activation('relu'))
    model = Sequential()
    model.add(Merge([left, right], mode='sum'))
    model.add(Dense(nb_class))
    model.add(Activation('softmax'))
    model.load_weights(fname)
    os.remove(fname)
    model.compile(loss='categorical_crossentropy', optimizer='rmsprop')

    nloss = model.evaluate([X_test, X_test], y_test, verbose=0)
    assert(loss == nloss)

    # test serialization
    config = model.get_config()
    Sequential.from_config(config)

    model.summary()
    json_str = model.to_json()
    model_from_json(json_str)

    yaml_str = model.to_yaml()
    model_from_yaml(yaml_str)

# 需要导入模块: from keras.models import Sequential [as 别名]
# 或者: from keras.models.Sequential import evaluate [as 别名]
    def test_merge_concat(self):
        print('Test merge: concat')
        left = Sequential()
        left.add(Dense(input_dim, nb_hidden))
        left.add(Activation('relu'))

        right = Sequential()
        right.add(Dense(input_dim, nb_hidden))
        right.add(Activation('relu'))

        model = Sequential()
        model.add(Merge([left, right], mode='concat'))

        model.add(Dense(nb_hidden * 2, nb_class))
        model.add(Activation('softmax'))

        model.compile(loss='categorical_crossentropy', optimizer='rmsprop')

        model.fit([X_train, X_train], y_train, batch_size=batch_size, nb_epoch=nb_epoch, show_accuracy=True, verbose=0, validation_data=([X_test, X_test], y_test))
        model.fit([X_train, X_train], y_train, batch_size=batch_size, nb_epoch=nb_epoch, show_accuracy=False, verbose=0, validation_data=([X_test, X_test], y_test))
        model.fit([X_train, X_train], y_train, batch_size=batch_size, nb_epoch=nb_epoch, show_accuracy=True, verbose=0, validation_split=0.1)
        model.fit([X_train, X_train], y_train, batch_size=batch_size, nb_epoch=nb_epoch, show_accuracy=False, verbose=0, validation_split=0.1)
        model.fit([X_train, X_train], y_train, batch_size=batch_size, nb_epoch=nb_epoch, verbose=0)
        model.fit([X_train, X_train], y_train, batch_size=batch_size, nb_epoch=nb_epoch, verbose=0, shuffle=False)

        loss = model.evaluate([X_train, X_train], y_train, verbose=0)
        print('loss:', loss)
        if loss > 0.6:
            raise Exception('Score too low, learning issue.')
        preds = model.predict([X_test, X_test], verbose=0)
        classes = model.predict_classes([X_test, X_test], verbose=0)
        probas = model.predict_proba([X_test, X_test], verbose=0)
        print(model.get_config(verbose=1))

        print('test weight saving')
        model.save_weights('temp.h5', overwrite=True)
        left = Sequential()
        left.add(Dense(input_dim, nb_hidden))
        left.add(Activation('relu'))

        right = Sequential()
        right.add(Dense(input_dim, nb_hidden))
        right.add(Activation('relu'))

        model = Sequential()
        model.add(Merge([left, right], mode='concat'))

        model.add(Dense(nb_hidden * 2, nb_class))
        model.add(Activation('softmax'))

        model.compile(loss='categorical_crossentropy', optimizer='rmsprop')
        model.load_weights('temp.h5')

        nloss = model.evaluate([X_train, X_train], y_train, verbose=0)
        print(nloss)
        assert(loss == nloss)

# 需要导入模块: from keras.models import Sequential [as 别名]
# 或者: from keras.models.Sequential import evaluate [as 别名]
def test_siamese_1():
    (X_train, y_train), (X_test, y_test) = _get_test_data()
    left = Sequential()
    left.add(Dense(nb_hidden, input_shape=(input_dim,)))
    left.add(Activation('relu'))

    right = Sequential()
    right.add(Dense(nb_hidden, input_shape=(input_dim,)))
    right.add(Activation('relu'))

    model = Sequential()
    model.add(Siamese(Dense(nb_hidden), [left, right], merge_mode='sum'))
    model.add(Dense(nb_class))
    model.add(Activation('softmax'))
    model.compile(loss='categorical_crossentropy', optimizer='rmsprop')

    model.fit([X_train, X_train], y_train, batch_size=batch_size, nb_epoch=nb_epoch, show_accuracy=True, verbose=0, validation_data=([X_test, X_test], y_test))
    model.fit([X_train, X_train], y_train, batch_size=batch_size, nb_epoch=nb_epoch, show_accuracy=False, verbose=0, validation_data=([X_test, X_test], y_test))
    model.fit([X_train, X_train], y_train, batch_size=batch_size, nb_epoch=nb_epoch, show_accuracy=True, verbose=0, validation_split=0.1)
    model.fit([X_train, X_train], y_train, batch_size=batch_size, nb_epoch=nb_epoch, show_accuracy=False, verbose=0, validation_split=0.1)
    model.fit([X_train, X_train], y_train, batch_size=batch_size, nb_epoch=nb_epoch, verbose=0)
    model.fit([X_train, X_train], y_train, batch_size=batch_size, nb_epoch=nb_epoch, verbose=0, shuffle=False)

    loss = model.evaluate([X_test, X_test], y_test, verbose=0)
    assert(loss < 0.8)

    model.predict([X_test, X_test], verbose=0)
    model.predict_classes([X_test, X_test], verbose=0)
    model.predict_proba([X_test, X_test], verbose=0)
    model.get_config(verbose=0)

    # test weight saving
    fname = 'test_siamese_1.h5'
    model.save_weights(fname, overwrite=True)
    left = Sequential()
    left.add(Dense(nb_hidden, input_shape=(input_dim,)))
    left.add(Activation('relu'))

    right = Sequential()
    right.add(Dense(nb_hidden, input_shape=(input_dim,)))
    right.add(Activation('relu'))

    model = Sequential()
    model.add(Siamese(Dense(nb_hidden), [left, right], merge_mode='sum'))
    model.add(Dense(nb_class))
    model.add(Activation('softmax'))

    model.load_weights(fname)
    os.remove(fname)
    model.compile(loss='categorical_crossentropy', optimizer='rmsprop')

    nloss = model.evaluate([X_test, X_test], y_test, verbose=0)
    assert(loss == nloss)

# 需要导入模块: from keras.models import Sequential [as 别名]
# 或者: from keras.models.Sequential import evaluate [as 别名]
def train_given_optimiser(optimiser):
    model = Sequential()
    model.add(Dense(1, input_dim=500))
    model.add(Activation(activation='sigmoid'))
    model.compile(optimizer=optimiser, loss='binary_crossentropy', metrics=['accuracy'])
    data = np.random.random((1000, 500))
    labels = np.random.randint(2, size=(1000, 1))
    score = model.evaluate(data,labels, verbose=0)
    print( "Optimiser: ", optimiser )
    print( "Before Training:", list(zip(model.metrics_names, score)) )
    model.fit(data, labels, nb_epoch=10, batch_size=32, verbose=0)
    score = model.evaluate(data,labels, verbose=0)
    print( "After Training:", list(zip(model.metrics_names, score)) )

# 需要导入模块: from keras.models import Sequential [as 别名]
# 或者: from keras.models.Sequential import evaluate [as 别名]
    def test_merge_recursivity(self):
        print('Test merge recursivity')

        left = Sequential()
        left.add(Dense(nb_hidden, input_shape=(input_dim,)))
        left.add(Activation('relu'))

        right = Sequential()
        right.add(Dense(nb_hidden, input_shape=(input_dim,)))
        right.add(Activation('relu'))

        righter = Sequential()
        righter.add(Dense(nb_hidden, input_shape=(input_dim,)))
        righter.add(Activation('relu'))

        intermediate = Sequential()
        intermediate.add(Merge([left, right], mode='sum'))
        intermediate.add(Dense(nb_hidden))
        intermediate.add(Activation('relu'))

        model = Sequential()
        model.add(Merge([intermediate, righter], mode='sum'))
        model.add(Dense(nb_class))
        model.add(Activation('softmax'))
        model.compile(loss='categorical_crossentropy', optimizer='rmsprop')

        model.fit([X_train, X_train, X_train], y_train, batch_size=batch_size, nb_epoch=nb_epoch, show_accuracy=True, verbose=0, validation_data=([X_test, X_test, X_test], y_test))
        model.fit([X_train, X_train, X_train], y_train, batch_size=batch_size, nb_epoch=nb_epoch, show_accuracy=False, verbose=0, validation_data=([X_test, X_test, X_test], y_test))
        model.fit([X_train, X_train, X_train], y_train, batch_size=batch_size, nb_epoch=nb_epoch, show_accuracy=True, verbose=0, validation_split=0.1)
        model.fit([X_train, X_train, X_train], y_train, batch_size=batch_size, nb_epoch=nb_epoch, show_accuracy=False, verbose=0, validation_split=0.1)
        model.fit([X_train, X_train, X_train], y_train, batch_size=batch_size, nb_epoch=nb_epoch, verbose=0)
        model.fit([X_train, X_train, X_train], y_train, batch_size=batch_size, nb_epoch=nb_epoch, verbose=0, shuffle=False)

        loss = model.evaluate([X_train, X_train, X_train], y_train, verbose=0)
        print('loss:', loss)
        if loss > 0.7:
            raise Exception('Score too low, learning issue.')
        model.predict([X_test, X_test, X_test], verbose=0)
        model.predict_classes([X_test, X_test, X_test], verbose=0)
        model.predict_proba([X_test, X_test, X_test], verbose=0)
        model.get_config(verbose=0)

        fname = 'test_merge_recursivity_temp.h5'
        model.save_weights(fname, overwrite=True)
        model.load_weights(fname)
        os.remove(fname)

        nloss = model.evaluate([X_train, X_train, X_train], y_train, verbose=0)
        print(nloss)
        assert(loss == nloss)