<p>By default, Keras sets shuffle argument <code>True</code>, so you should set the numpy seed before importing Keras.</p>
<p><strong>CPU</strong></p>
<pre><code>from numpy.random import seed
seed(25)
from keras.models import Sequential
</code></pre>
<p><strong>GPU</strong></p>
<pre><code>tf.random.set_seed(seed)
</code></pre>

CC BY-SA 4.0
2021-01-26T05:50:37.563Z
Hi yahkyo, I set the random seed to a fixed value and rerun the whole model but the outcomes still changed every training. So I then set the shuffle = False, the outcomes were still not exactly the same but this time the trends were generally the same.
Leo
CC BY-SA 4.0
2021-01-26T05:58:53.473Z
Hi Leo, I think random shuffle in your code is on GPU. However numpy works on CPU. Have you tried  `tf.random.set_seed(seed)` ?
yakhyo
CC BY-SA 4.0
2021-01-26T06:14:36.813Z
You are right, I was running GPU. `tf.random.set_seed(seed)` this code doesn't give the 100% same outcomes, but I think  95% similarity is good for parameter adjustment. Thanks.
Leo
CC BY-SA 4.0
2021-01-26T05:17:32.227Z
2021-01-26T15:02:15.867Z
2021-01-26T15:02:15.867Z
yakhyo
<p>Here is a model that trains time series data in (batch, step, features) way.</p>
<p>I have kept the random state for train test split function the same. Every parameter below the same, running the model training yields different outcomes every time and the outcomes are drastically different.</p>
<p>What may be the factors that led to this? Regularization?</p>
<p><a href="https://i.sstatic.net/rWl8m.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/rWl8m.png" alt="outcome_1" /></a>
<a href="https://i.sstatic.net/rLM2N.jpg" rel="nofollow noreferrer"><img src="https://i.sstatic.net/rLM2N.jpg" alt="outcome_2" /></a>
<a href="https://i.sstatic.net/Cpmpt.jpg" rel="nofollow noreferrer"><img src="https://i.sstatic.net/Cpmpt.jpg" alt="outcome_3" /></a></p>
<p><code>X_train, X_val, y_train, y_val = train_test_split(X_train, y_train, test_size=0.2, random_state=666)</code></p>
<pre><code>def attention_model(X_train, y_train, X_test, y_test,num_classes,dropout=0.2, batch_size=68, learning_rate=0.0001,epochs=20,optimizer='Adam'):
    
    Dense_unit = 12
    LSTM_unit = 12
    
    attention_param = LSTM_unit*2
    attention_init_value = 1.0/attention_param
    
    
    u_train = np.full((X_train.shape[0], attention_param),
                      attention_init_value, dtype=np.float32)
    u_test = np.full((X_test.shape[0],attention_param),
                     attention_init_value, dtype=np.float32)
    
    
    with keras.backend.name_scope('BLSTMLayer'):
        # Bi-directional Long Short-Term Memory for learning the temporal aggregation
        input_feature = Input(shape=(X_train.shape[1],X_train.shape[2]))
        x = Masking(mask_value=0)(input_feature)
        x = Dense(Dense_unit,kernel_regularizer=l2(0.005), activation='relu')(x)
        x = Dropout(dropout)(x)
        x = Dense(Dense_unit,kernel_regularizer=l2(0.005),activation='relu')(x)
        x = Dropout(dropout)(x)
        x = Dense(Dense_unit,kernel_regularizer=l2(0.005),activation='relu')(x)
        x = Dropout(dropout)(x)
        x = Dense(Dense_unit,kernel_regularizer=l2(0.005), activation='relu')(x)
        x = Dropout(dropout)(x)


        y = Bidirectional(LSTM(LSTM_unit,activity_regularizer=l2(0.000029),kernel_regularizer=l2(0.027),recurrent_regularizer=l2(0.025),return_sequences=True, dropout=dropout))(x)
#         y = Bidirectional(LSTM(LSTM_unit, kernel_regularizer=l2(0.01),recurrent_regularizer=l2(0.01), return_sequences=True, dropout=dropout))(y)

    with keras.backend.name_scope('AttentionLayer'):
        # Logistic regression for learning the attention parameters with a standalone feature as input
        input_attention = Input(shape=(LSTM_unit * 2,))
        u = Dense(LSTM_unit * 2, activation='softmax')(input_attention)

        # To compute the final weights for the frames which sum to unity
        alpha = dot([u, y], axes=-1)  # inner prod.
        alpha = Activation('softmax')(alpha)

    with keras.backend.name_scope('WeightedPooling'):
        # Weighted pooling to get the utterance-level representation
        z = dot([alpha, y], axes=1)

    # Get posterior probability for each emotional class
    output = Dense(num_classes, activation='softmax')(z)

    model = Model(inputs=[input_attention, input_feature], outputs=output)

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


    hist = model.fit([u_train, X_train], 
                     y_train, 
                     batch_size=batch_size, 
                     epochs=epochs, 
                     verbose=2, 
                     validation_data=([u_test, X_test], y_test))
    

#kernel_regularizer=l2(0.002),recurrent_regularizer=l2(0.002),
    return hist

</code></pre>
<pre><code>batch_size= 150
#217
epochs = 1000
learning_rate = 0.00081
optimizer = 'RMS'
num_classes = y_train.shape[1]
dropout=0.22

tf.keras.backend.clear_session()

history = attention_model(X_train, y_train, X_test, y_test, num_classes,dropout = dropout,batch_size=batch_size, learning_rate=learning_rate,epochs=epochs,optimizer=optimizer
)
</code></pre>

CC BY-SA 4.0
2021-01-26T15:05:10.773Z
Hello. Welcome to AI SE. I don't know if this is "just" a programming issue or not, but I would like you to note that programming questions (like "Why am I getting this programming error/bug?") are generally off-topic here. These questions are better suited for Stack Overflow. I don't know if your question is a pure programming issue (because I didn't read it, but it contains code...), but just keep this in mind. You should read https://ai.stackexchange.com/help/on-topic for more info.
nbro
CC BY-SA 4.0
2021-01-27T01:10:49.263Z
@nbro Hi nbro, I was not sure what was causing the problem. My initial thought was that the problem was caused by some randomness of models not because of codes so I post it here.
Leo
<machine-learning><deep-learning><recurrent-neural-networks><attention>
Factors that causing totally different outcomes from an exactly same model and datasets
Leo
# Factors that causing totally different outcomes from an exactly same model and datasets

(Asked by: username_0 on 2021-01-26)

Here is a model that trains time series data in (batch, step, features) way.
I have kept the random state for train test split function the same. Every parameter below the same, running the model training yields different outcomes every time and the outcomes are drastically different.
What may be the factors that led to this? Regularization?



X_train, X_val, y_train, y_val = train_test_split(X_train, y_train, test_size=0.2, random_state=666)
def attention_model(X_train, y_train, X_test, y_test,num_classes,dropout=0.2, batch_size=68, learning_rate=0.0001,epochs=20,optimizer='Adam'):
    
    Dense_unit = 12
    LSTM_unit = 12
    
    attention_param = LSTM_unit*2
    attention_init_value = 1.0/attention_param
    
    
    u_train = np.full((X_train.shape[0], attention_param),
                      attention_init_value, dtype=np.float32)
    u_test = np.full((X_test.shape[0],attention_param),
                     attention_init_value, dtype=np.float32)
    
    
    with keras.backend.name_scope('BLSTMLayer'):
        # Bi-directional Long Short-Term Memory for learning the temporal aggregation
        input_feature = Input(shape=(X_train.shape[1],X_train.shape[2]))
        x = Masking(mask_value=0)(input_feature)
        x = Dense(Dense_unit,kernel_regularizer=l2(0.005), activation='relu')(x)
        x = Dropout(dropout)(x)
        x = Dense(Dense_unit,kernel_regularizer=l2(0.005),activation='relu')(x)
        x = Dropout(dropout)(x)
        x = Dense(Dense_unit,kernel_regularizer=l2(0.005),activation='relu')(x)
        x = Dropout(dropout)(x)
        x = Dense(Dense_unit,kernel_regularizer=l2(0.005), activation='relu')(x)
        x = Dropout(dropout)(x)


        y = Bidirectional(LSTM(LSTM_unit,activity_regularizer=l2(0.000029),kernel_regularizer=l2(0.027),recurrent_regularizer=l2(0.025),return_sequences=True, dropout=dropout))(x)
#         y = Bidirectional(LSTM(LSTM_unit, kernel_regularizer=l2(0.01),recurrent_regularizer=l2(0.01), return_sequences=True, dropout=dropout))(y)

    with keras.backend.name_scope('AttentionLayer'):
        # Logistic regression for learning the attention parameters with a standalone feature as input
        input_attention = Input(shape=(LSTM_unit * 2,))
        u = Dense(LSTM_unit * 2, activation='softmax')(input_attention)

        # To compute the final weights for the frames which sum to unity
        alpha = dot([u, y], axes=-1)  # inner prod.
        alpha = Activation('softmax')(alpha)

    with keras.backend.name_scope('WeightedPooling'):
        # Weighted pooling to get the utterance-level representation
        z = dot([alpha, y], axes=1)

    # Get posterior probability for each emotional class
    output = Dense(num_classes, activation='softmax')(z)

    model = Model(inputs=[input_attention, input_feature], outputs=output)

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


    hist = model.fit([u_train, X_train], 
                     y_train, 
                     batch_size=batch_size, 
                     epochs=epochs, 
                     verbose=2, 
                     validation_data=([u_test, X_test], y_test))
    

#kernel_regularizer=l2(0.002),recurrent_regularizer=l2(0.002),
    return hist


batch_size= 150
#217
epochs = 1000
learning_rate = 0.00081
optimizer = 'RMS'
num_classes = y_train.shape[1]
dropout=0.22

tf.keras.backend.clear_session()

history = attention_model(X_train, y_train, X_test, y_test, num_classes,dropout = dropout,batch_size=batch_size, learning_rate=learning_rate,epochs=epochs,optimizer=optimizer
)

**Tags:** machine-learning, deep-learning, recurrent-neural-networks, attention

> **Comments:**
> **username_1**: Hello. Welcome to AI SE. I don't know if this is "just" a programming issue or not, but I would like you to note that programming questions (like "Why am I getting this programming error/bug?") are generally off-topic here. These questions are better suited for Stack Overflow. I don't know if your question is a pure programming issue (because I didn't read it, but it contains code...), but just keep this in mind. You should read https://ai.stackexchange.com/help/on-topic for more info.  
> **username_0**: @username_1 Hi username_1, I was not sure what was causing the problem. My initial thought was that the problem was caused by some randomness of models not because of codes so I post it here.  

---

## 1 answers

### Answer by username_2 on 2021-01-26. Score: 0 | Accepted answer

By default, Keras sets shuffle argument True, so you should set the numpy seed before importing Keras.
CPU
from numpy.random import seed
seed(25)
from keras.models import Sequential

GPU
tf.random.set_seed(seed)

> **Comments:**
> **username_0**: Hi yahkyo, I set the random seed to a fixed value and rerun the whole model but the outcomes still changed every training. So I then set the shuffle = False, the outcomes were still not exactly the same but this time the trends were generally the same.  
> **username_2**: Hi username_0, I think random shuffle in your code is on GPU. However numpy works on CPU. Have you tried  `tf.random.set_seed(seed)` ?  
> **username_0**: You are right, I was running GPU. `tf.random.set_seed(seed)` this code doesn't give the 100% same outcomes, but I think  95% similarity is good for parameter adjustment. Thanks.  

---

