I want to apply GridSearchCV on the autoencoder model. The code of the atuoencoder and GridSearchCV is added below please tell me how I change this code to run GridSearchCV successfully.
autoencoder = Sequential()
# Encoder Layers
autoencoder.add(Conv2D(16, (3, 3), activation='relu', padding='same', input_shape=x_train.shape[1:]))
autoencoder.add(MaxPooling2D((2, 2), padding='same'))
autoencoder.add(Conv2D(8, (3, 3), activation='relu', padding='same'))
autoencoder.add(MaxPooling2D((2, 2), padding='same'))
autoencoder.add(Conv2D(8, (3, 3), strides=(2,2), activation='relu', padding='same'))
# Flatten encoding for visualization
autoencoder.add(Flatten())
autoencoder.add(Reshape((4, 4, 8)))
# Decoder Layers
autoencoder.add(Conv2D(8, (3, 3), activation='relu', padding='same'))
autoencoder.add(UpSampling2D((2, 2)))
autoencoder.add(Conv2D(8, (3, 3), activation='relu', padding='same'))
autoencoder.add(UpSampling2D((2, 2)))
autoencoder.add(Conv2D(16, (3, 3), activation='relu'))
autoencoder.add(UpSampling2D((2, 2)))
autoencoder.add(Conv2D(1, (3, 3), activation='sigmoid', padding='same'))
autoencoder.summary()
I want to apply GridSearch on the above autoencoder code
from sklearn.model_selection import GridSearchCV
from keras.wrappers.scikit_learn import KerasClassifier
model_classifier = KerasClassifier(autoencoder, verbose=1, batch_size=10, epochs=10)
# define the grid search parameters
batch_size = [10]
loss = ['mean_squared_error', 'binary_crossentropy']
optimizer = [Adam, SGD, RMSprop]
learning_rate = [0.001]
epochs = [3, 5]
param_grid = dict(optimizer=optimizer, learning_rate=learning_rate)
grid = GridSearchCV(cv=[(slice(None), slice(None))], estimator=model_classifier, param_grid=param_grid, n_jobs=1)
grid_result = grid.fit(x_train, x_train)
print("training Successfully completed")
I have solved this by hard code. I applied for lop on every parameter and get the result. For best parameter selection I have find the parameter on which I have got high results.