在Keras中實現文本生成任務通常使用循環神經網絡（RNN）或者長短時記憶網絡（LSTM）。以下是一個簡單的例子，以生成莎士比亞風格的文本為例：

1. 導入必要的庫和模塊：

from keras.models import Sequential
from keras.layers import LSTM, Dense

2. 準備數據：

text = # 輸入文本數據

maxlen = 40
step = 3
sentences = []
next_chars = []

for i in range(0, len(text) - maxlen, step):
    sentences.append(text[i: i + maxlen])
    next_chars.append(text[i + maxlen])

x = np.zeros((len(sentences), maxlen, len(chars)), dtype=np.bool)
y = np.zeros((len(sentences), len(chars)), dtype=np.bool)

for i, sentence in enumerate(sentences):
    for t, char in enumerate(sentence):
        x[i, t, char_indices[char]] = 1
    y[i, char_indices[next_chars[i]]] = 1

3. 構建模型：

model = Sequential()
model.add(LSTM(128, input_shape=(maxlen, len(chars)))
model.add(Dense(len(chars), activation='softmax'))

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

4. 訓練模型：

model.fit(x, y, batch_size=128, epochs=60)

5. 生成文本：

def generate_text(model, start_string, length=400, temperature=0.5):
    generated = start_string
    for i in range(length):
        x_pred = np.zeros((1, maxlen, len(chars)))
        for t, char in enumerate(start_string):
            x_pred[0, t, char_indices[char]] = 1.
        
        preds = model.predict(x_pred, verbose=0)[0]
        next_index = sample(preds, temperature)
        next_char = indices_char[next_index]
        
        generated += next_char
        start_string = start_string[1:] + next_char
    return generated

以上是一個簡單的文本生成任務的實現步驟，可以根據具體需求和數據進行調整和優化。