CODEWITHSUNDEEP
pythontensorflowkeras
Tom Leung
Tom Leung

Reputation: 374

'Sequential' object has no attribute 'loss' - When I used GridSearchCV to tuning my Keras model

I am using tensorflow and keras to build a simple MNIST classification model, and I want to fine tune my model, so I choose sklearn.model_selection.GridSearchCV.

However, when I call the fit function, it said:

AttributeError: 'Sequential' object has no attribute 'loss'

I compared my code to others', but still can't figure out why. The only difference is that I use tensorflow.keras instead of keras.

Here is my code:

from tensorflow.keras.models import Sequential, Model
from tensorflow.keras.layers import Input, Dense, Activation, Dropout, BatchNormalization
from tensorflow.keras.datasets import mnist
from tensorflow.keras.wrappers.scikit_learn import KerasClassifier
from sklearn.model_selection import GridSearchCV

...
...
...


def get_model(dropout_rate=0.2, hidden_units=512):
    model = Sequential()
    model.add(Dropout(dropout_rate, input_shape=(28*28,)))
    model.add(Dense(hidden_units, activation='relu'))
    model.add(BatchNormalization())
    model.add(Dropout(dropout_rate))
    model.add(Dense(hidden_units, activation='relu'))
    model.add(BatchNormalization())
    model.add(Dropout(dropout_rate))
    model.add(Dense(hidden_units, activation='relu'))
    model.add(BatchNormalization())
    model.add(Dropout(dropout_rate))
    model.add(Dense(10, activation='softmax'))
    return model

model = KerasClassifier(build_fn=get_model, batch_size=128, epochs=10)
para_dict = {'dropout_rate':[0.2,0.5,0.8], 'hidden_units':[128,256,512,1024]}
clf = GridSearchCV(model, para_dict, cv=5, scoring='accuracy')
clf.fit(x_train, y_train)

Upvotes: 9

Views: 9181

Answers (1)

Nihal Sangeeth
Nihal Sangeeth

Reputation: 5515

The build_model function above doesn't configure your model for training. You have added loss and other parameters.

You can compile the model by using keras sequential method compile. https://keras.io/models/sequential/

So your build_model function should be:

loss = 'binary_crossentropy' #https://keras.io/optimizers
optimizer = 'adam'           #https://keras.io/losses
metrics = ['accuracy']
def get_model(dropout_rate=0.2, hidden_units=512):
    model = Sequential()
    model.add(Dropout(dropout_rate, input_shape=(28*28,)))
    model.add(Dense(hidden_units, activation='relu'))
    model.add(BatchNormalization())
    model.add(Dropout(dropout_rate))
    model.add(Dense(hidden_units, activation='relu'))
    model.add(BatchNormalization())
    model.add(Dropout(dropout_rate))
    model.add(Dense(hidden_units, activation='relu'))
    model.add(BatchNormalization())
    model.add(Dropout(dropout_rate))
    model.add(Dense(10, activation='softmax'))
    model.compile(optimizer = optimizer, loss = loss, metrics = metrics)
    return model

Upvotes: 8

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