I am trying to create a convolutional neural network using GridSearchCV and the scikeras wrapper, but I have been receiving an error that I cannot figure out the cause of.
The core of the error is this:
ValueError: Invalid parameter layers for estimator KerasClassifier. This issue can likely be resolved by setting this parameter in the KerasClassifier constructor:
KerasClassifier(layers=[128])
Check the list of available parameters withestimator.get_params().keys()
Please find the full error after the code. I have tried changing a few line, or adding different parameters, however nothing seems to change the error I am receiving. Here is the code:
# first model using the chosen parameters
# Part 1: Create the model
def cnn_model0(layers):
cnn = tf.keras.models.Sequential() # initialising the CNN
# model layers
cnn.add( # Step 1 - Convolution
tf.keras.layers.Conv2D(filters=32, kernel_size=3, padding="same", activation="relu", input_shape=[50, 50, 3]))
cnn.add( # Step 2 - Pooling
tf.keras.layers.MaxPool2D(pool_size=2, strides=2, padding='valid'))
cnn.add( # Second convolutional layer
tf.keras.layers.Conv2D(filters=32, kernel_size=3, padding="same", activation="relu"))
cnn.add( # Second pooling layer
tf.keras.layers.MaxPool2D(pool_size=2, strides=2, padding='valid'))
cnn.add( # Step 3 - Flattening
tf.keras.layers.Flatten())
# Step 4 - Full connection (FC)
for i, nodes in enumerate(layers):
cnn.add(tf.keras.layers.Dense(units = nodes, activation = 'relu'))
cnn.add(tf.keras.layers.Dense(units = 43, activation = 'softmax'))
# Compiling the CNN
cnn.compile(optimizer = 'Adam', loss = 'categorical_crossentropy', metrics = ['accuracy'])
return cnn
# Part 2: Fitting the CNN model
model = KerasClassifier(build_fn = cnn_model0, verbose = 1)
# establish the grid parameters
layers = [[128], (256, 128), (200, 150, 120)]
param_grid = dict(layers = layers)
# fit GridSearchCV
grid = GridSearchCV(estimator = model, param_grid = param_grid, verbose = 1)
grid_results = grid.fit(X_train, y_train, validation_data = (X_val, y_val))
# Part 3: Printing the results
print("Best: {0}, using {1}".format(grid_results.best_score_, grid_results.best_params_))
# result values
means = grid_results.cv_results_['mean_test_score']
stds = grid_results.cv_results_['std_test_score']
params = grid_results.cv_results_['params']
for mean, stdev, param in zip(means, stds, params):
print('{0} ({1}) with: {2}'.format(mean, stdev, param))
The main section that seems to be the cause of the error is:
model = KerasClassifier(build_fn = cnn_model0, verbose = 1)
layers = [[128], (256, 128), (200, 150, 120)]
param_grid = dict(layers = layers)
grid = GridSearchCV(estimator = model, param_grid = param_grid, verbose = 1)
grid_results = grid.fit(X_train, y_train, validation_data = (X_val, y_val))
From the message I am receiving, I am thinking there is something incorrect within the model, and the layers being established. Any help on narrowing down the cause would be greatly appreciated. I am still unfamiliar with a lot of machine learning.
Thanks in advance.
Full Error Message:
Fitting 5 folds for each of 3 candidates, totalling 15 fits
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-33-b6a6389b51ee> in <module>()
35 # fit GridSearchCV
36 grid = GridSearchCV(estimator = model, param_grid = param_grid, verbose = 1)
---> 37 grid_results = grid.fit(X_train, y_train, validation_data = (X_val, y_val))
38
39 # Part 3: Printing the results
12 frames
/usr/local/lib/python3.7/dist-packages/scikeras/wrappers.py in set_params(self, **params)
1153 "\nCheck the list of available parameters with"
1154 " `estimator.get_params().keys()`"
-> 1155 ) from None
1156 return self
1157
ValueError: Invalid parameter layers for estimator KerasClassifier.
This issue can likely be resolved by setting this parameter in the KerasClassifier constructor:
`KerasClassifier(layers=[128])`
Check the list of available parameters with `estimator.get_params().keys()`
CodePudding user response:
It works now after making the change stated in the error. The code is changed from:
# Part 2: Fitting the CNN model
model = KerasClassifier(build_fn = cnn_model0, verbose = 1)
# establish the grid parameters
layers = [[128], (256, 128), (200, 150, 120)]
param_grid = dict(layers = layers)
To
# Part 2: Fitting the CNN model
model = KerasClassifier(build_fn = cnn_model0, verbose = 1, layers = [[128], (256, 128), (200, 150, 120)])
# establish the grid parameters
param_grid = dict(layers = layers)
Although there is now another issue, 'layers' is no longer defined for 'param_grid = dict(layers = layers)', but the model still produces results despite this.