python - CNN model converges very fast -

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i training cnn model text dataset(domain names) of 1 million entries. model seem converge fast. gives me 94% accuracy in 1 epoch. ideas why happening? 

def build_model(max_features, maxlen):     """build cnn model"""     model = sequential()     model.add(embedding(max_features, 8, input_length=maxlen))     model.add(convolution1d(6, 4, border_mode='same'))     model.add(convolution1d(4, 4, border_mode='same'))     model.add(convolution1d(2, 4, border_mode='same'))     model.add(flatten())     #model.add(dropout(0.2))     #model.add(dense(2,activation='sigmoid'))     #model.add(dense(180,activation='sigmoid'))     #model.add(dropout(0.2))     model.add(dense(2,activation='softmax'))      return model

output during first 3 epochs:

950000/950000 [==============================] - 232s - loss: 0.1764 - categorical_accuracy: 0.9356 - fmeasure: 0.9356 - precision: 0.9356 - recall: 0.9356 - val_loss: 0.1579 - val_categorical_accuracy: 0.9418 - val_fmeasure: 0.9418 - val_precision: 0.9418 - val_recall: 0.9418 epoch 2/3 950000/950000 [==============================] - 232s - loss: 0.1567 - categorical_accuracy: 0.9450 - fmeasure: 0.9450 - precision: 0.9450 - recall: 0.9450 - val_loss: 0.1518 - val_categorical_accuracy: 0.9489 - val_fmeasure: 0.9489 - val_precision: 0.9489 - val_recall: 0.9489 epoch 3/3 950000/950000 [==============================] - 232s - loss: 0.1515 - categorical_accuracy: 0.9474 - fmeasure: 0.9474 - precision: 0.9474 - recall: 0.9474 - val_loss: 0.1474 - val_categorical_accuracy: 0.9472 - val_fmeasure: 0.9472 - val_precision: 0.9472 - val_recall: 0.9472 3392/3801 [=========================>....] - eta: 0s[0.15151389103144666, 0.94817153352462946, 0.94817148384625149, 0.94817153391666209, 0.94817153391666209]





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