I created a multiple linear regression model in keras. I have been trying to use the model.save() and model.load_model() methods. I tried both the 'tf' and the 'h5' format but neither seem to work.
Calling .summary() on the loaded 'tf' model generates the following:
ValueError: This model has not yet been built. Build the model first by calling build() or calling fit() with some data, or specify an input_shape argument in the first layer(s) for automatic build.
The 'h5' model throws an error during loading:
ValueError: You are trying to load a weight file containing 2 layers into a model with 0 layers.
I found postings of similar issues but the solutions don't seem to work in my case.
Here's the code I used to build the model.
# Create an empty list that will eventually hold all created feature columns.
feature_columns = []
feature_names = data.drop(['price', 'lat', 'long'], axis=1).columns
# Loop through features to represent data
for feature in feature_names:
if data[feature].dtype == bool:
train_df_norm[feature] = data[feature].astype('str') # bool raises value error
test_df_norm[feature] = data[feature].astype('str') # bool raises value error
categorical_feature = tf.feature_column.categorical_column_with_vocabulary_list(
feature, ['True', 'False']
)
new_feature = tf.feature_column.indicator_column(categorical_feature)
else:
new_feature = tf.feature_column.numeric_column(feature, shape=(1,))
feature_columns.append(new_feature)
# Convert list of feature columns into a layer that will be fed into the model.
my_feature_layer = tf.keras.layers.DenseFeatures(feature_columns)
def create_model(my_learning_rate, feature_layer, l2=0):
"""Create and compile a linear regression model with l2 regularization."""
# Most simple tf.keras models are sequential.
model = tf.keras.models.Sequential()
# Add the layer containing the feature columns to the model.
model.add(feature_layer)
# model.add(keras.engine.InputLayer(batch_input_shape=(36, 1)))
# Add one linear layer to the model to yield a simple linear regressor.
model.add(tf.keras.layers.Dense(units=1, input_shape=(1,),
kernel_regularizer=tf.keras.regularizers.l2(l2)))
# Construct the layers into a model that TensorFlow can execute.
model.compile(optimizer=tf.keras.optimizers.RMSprop(lr=my_learning_rate),
loss="mean_squared_error",
metrics=[tf.keras.metrics.MeanSquaredError()])
return model
def train_model(model, dataset, epochs, batch_size, label_name):
"""Feed a dataset into the model in order to train it."""
# Split the dataset into features and label.
features = {name:np.array(value) for name, value in dataset.items()}
label = np.array(features.pop(label_name))
history = model.fit(x=features, y=label, batch_size=batch_size,
epochs=epochs, shuffle=True)
# Get details that will be useful for plotting the loss curve.
epochs = history.epoch
hist = pd.DataFrame(history.history)
rmse = hist["mean_squared_error"]
return epochs, rmse
print("Defined the create_model and train_model functions.")
# Hyperparameters.
learning_rate = 0.002
epochs = 150
batch_size = 1000
l2 = 0.1
label_name = "price"
# Establish the model's topography.
my_linear_model = create_model(learning_rate, my_feature_layer, l2)
# Train the model on the normalized training set.
epochs, mse = train_model(my_linear_model, train_df_norm, epochs, batch_size, label_name)
# Validation
test_features = {name:np.array(value) for name, value in test_df_norm.items()}
test_label = np.array(test_features.pop(label_name)) # isolate the label
print("\n Evaluate the linear regression model against the test set:")
my_linear_model.evaluate(x = test_features, y = test_label, batch_size=batch_size)
