Keras 模型分类为每个模型重新编译返回不同的结果

Question

I'm starting with Keras creating a model to classify text labels by inputting a couple of text features with a single output. I've a specific function to create the model and another one to test the model using a different dataset.

I'm still trying to fine tune the model predictions but i'd like to try understand why my test function is getting different results every time the model is recreated. Is that usual ? Also, i'd appreciate any tip to improve the model accuracy.

def create_model(model_name,data,test_data):

# lets take 80% data as training and remaining 20% for test.
train_size = int(len(data) * .9)
test_size = int(len(data) * .4)

train_headlines = data['Subject']
train_category = data['Category']
train_activities = data['Activity']

test_headlines = data['Subject'][:test_size]
test_category = data['Category'][:test_size]
test_activities = data['Activity'][:test_size]
# define Tokenizer with Vocab Sizes
vocab_size1 = 10000 
vocab_size2 = 5000
batch_size = 100
tokenizer = Tokenizer(num_words=vocab_size1)
tokenizer2 = Tokenizer(num_words=vocab_size2)

test_headlines=test_headlines.astype(str)
train_headlines=train_headlines.astype(str)
test_category=test_category.astype(str)
train_category=train_category.astype(str)

tokenizer.fit_on_texts(test_headlines)
tokenizer2.fit_on_texts(test_category)
x_train = tokenizer.texts_to_matrix(train_headlines, mode='tfidf')
x_test = tokenizer.texts_to_matrix(test_headlines, mode='tfidf')

y_train = tokenizer2.texts_to_matrix(train_category, mode='tfidf')
y_test = tokenizer2.texts_to_matrix(test_category, mode='tfidf')


# load classes
labels = []
encoder = LabelBinarizer()
encoder.fit(train_activities)
text_labels = encoder.classes_    
with open('outputs/classes.txt', 'w') as f:
    for item in text_labels:
        f.write("%s\n" % item)  
z_train = encoder.transform(train_activities)
z_test = encoder.transform(test_activities)
num_classes = len(text_labels)
print ("num_classes: "+str(num_classes))


input1 = Input(shape=(vocab_size1,), name='main_input')
x1 = Dense(512, activation='relu')(input1)
x1 = Dense(64, activation='relu')(x1)
x1 = Dense(64, activation='relu')(x1)
input2 = Input(shape=(vocab_size2,), name='cat_input')
main_output = Dense(num_classes, activation='softmax', name='main_output')(x1)

model = Model(inputs=[input1, input2], outputs=[main_output])

model.compile(loss='categorical_crossentropy',
            optimizer='adam',
            metrics=['accuracy'])

model.summary() 
history = model.fit([x_train,y_train], z_train,
                    batch_size=batch_size,
                    epochs=30,
                    verbose=1,
                    validation_split=0.1)
score = model.evaluate([x_test,y_test], z_test,
    batch_size=batch_size, verbose=1)

print('Test accuracy:', score[1])

# serialize model to JSON
model_json = model.to_json()
with open("./outputs/my_model_"+model_name+".json", "w") as json_file:
    json_file.write(model_json)
# creates a HDF5 file 'my_model.h5'
model.save('./outputs/my_model_'+model_name+'.h5')

# Save Tokenizer i.e. Vocabulary
with open('./outputs/tokenizer'+model_name+'.pickle', 'wb') as handle:
    pickle.dump(tokenizer, handle, protocol=pickle.HIGHEST_PROTOCOL)

def validate_model (model_name,test_data,labels):

from keras.models import model_from_json
test_data['Subject'] = test_data['Subject'] + " " + test_data['Description']
headlines = test_data['Subject'].astype(str)     
categories = test_data['Category'].astype(str)

# load json and create model
json_file = open("./outputs/my_model_"+model_name+".json", 'r') 
loaded_model_json = json_file.read()
json_file.close()
model = model_from_json(loaded_model_json)
# load weights into new model
model.load_weights('./outputs/my_model_'+model_name+'.h5')
print("Loaded model from disk")
# loading
import pickle
with open('./outputs/tokenizer'+model_name+'.pickle', 'rb') as handle:
    tokenizer = pickle.load(handle)    
# Subjects 
x_pred = tokenizer.texts_to_matrix(headlines, mode='tfidf')
# Categorias
y_pred = tokenizer.texts_to_matrix(categories, mode='tfidf')  
predictions = []
scores = []
predictions_vetor = model.predict({'main_input': x_pred, 'cat_input': y_pred}) 

Answer 1

I read your training code following.

model.fit([x_train,y_train], z_train,
                batch_size=batch_size,
                epochs=30,
                verbose=1,
                validation_split=0.1)

You are using [x_train, y_train] as features and z_train as labels for your model. y_train is the raw form of label and z_train is the encoded form of label.

This way you are leaking information to the training set, hence resulting in an over-fitting situation. You model is not generalised at all, and therefore predicting irrelevant results.