1，一般描述
we saw that the accuracy of our model on the validation data would peak after training for a number of epochs, and would then start decreasing.
In other words, our model would overfit to the training data. Learning how to deal with overfitting is important. Although it's often possible to achieve high accuracy on the training set, what we really want is to develop models that generalize well to a testing data (or data they haven't seen before).
The opposite of overfitting is underfitting. Underfitting occurs when there is still room for improvement on the test data. This can happen for a number of reasons: If the model is not powerful enough, is over-regularized, or has simply not been trained long enough. This means the network has not learned the relevant patterns in the training data
We need to strike a balance
2，減少過擬合
更多數據，簡化模型，正則化，丟棄一部分
3，句子變多熱
NUM_WORDS = 10000
(train_data, train_labels), (test_data, test_labels) = keras.datasets.imdb.load_data(num_words=NUM_WORDS)
def multi_hot_sequences(sequences, dimension):
# Create an all-zero matrix of shape (len(sequences), dimension)
results = np.zeros((len(sequences), dimension))
for i, word_indices in enumerate(sequences):
results[i, word_indices] = 1.0 # set specific indices of results[i] to 1s
if i==100:
print(results)
return results
train_data = multi_hot_sequences(train_data, dimension=NUM_WORDS)
test_data = multi_hot_sequences(test_data, dimension=NUM_WORDS)
4，檢查有多少個單詞
np.sum(test_data[1000])或者np.nonzero(test_data[0])
5，三個模型
正好，過復雜，過簡單
6，history可用
bigger_history = bigger_model.fit(train_data, train_labels,
epochs=20,
batch_size=512,
validation_data=(test_data, test_labels),
verbose=2)
7，解決過擬合的手段之L2
l2_model = keras.models.Sequential([
keras.layers.Dense(16, kernel_regularizer=keras.regularizers.l2(0.001),
activation=tf.nn.relu, input_shape=(NUM_WORDS,)),
keras.layers.Dense(16, kernel_regularizer=keras.regularizers.l2(0.001),
activation=tf.nn.relu),
keras.layers.Dense(1, activation=tf.nn.sigmoid)
])
l2_model.compile(optimizer='adam',
loss='binary_crossentropy',
metrics=['accuracy', 'binary_crossentropy'])
l2_model_history = l2_model.fit(train_data, train_labels,
epochs=20,
batch_size=512,
validation_data=(test_data, test_labels),
verbose=2)
8，解決過擬合的手段之dropout
dpt_model = keras.models.Sequential([
keras.layers.Dense(16, activation=tf.nn.relu, input_shape=(NUM_WORDS,)),
keras.layers.Dropout(0.5),
keras.layers.Dense(16, activation=tf.nn.relu),
keras.layers.Dropout(0.5),
keras.layers.Dense(1, activation=tf.nn.sigmoid)
])
dpt_model.compile(optimizer='adam',
loss='binary_crossentropy',
metrics=['accuracy','binary_crossentropy'])
dpt_model_history = dpt_model.fit(train_data, train_labels,
epochs=20,
batch_size=512,
validation_data=(test_data, test_labels),
verbose=2)