I have been practicing building and comparing neural networks using Keras and Tensorflow in python, but when I look to plot the models for comparisons I am receiving an error:
TypeError: 'History' object is not subscriptable
Here is my code for the three models:
############################## Initiate model 1 ###############################
# Model 1 has no hidden layers
from keras.models import Sequential
model1 = Sequential()
# Get layers
from keras.layers import Dense
# Add first layer
n_cols = len(X.columns)
model1.add(Dense(units=n_cols, activation='relu', input_shape=(n_cols,)))
# Add output layer
model1.add(Dense(units=2, activation='softmax'))
# Compile the model
model1.compile(loss='categorical_crossentropy', optimizer='adam', metrics=
['accuracy'])
# Define early_stopping_monitor
from keras.callbacks import EarlyStopping
early_stopping_monitor = EarlyStopping(patience=2)
# Fit model
model1.fit(X, y, validation_split=0.33, epochs=30, callbacks=
[early_stopping_monitor], verbose=False)
############################## Initiate model 2 ###############################
# Model 2 has 1 hidden layer that has the mean number of nodes of input and output layer
model2 = Sequential()
# Add first layer
model2.add(Dense(units=n_cols, activation='relu', input_shape=(n_cols,)))
# Add hidden layer
import math
model2.add(Dense(units=math.ceil((n_cols+2)/2), activation='relu'))
# Add output layer
model2.add(Dense(units=2, activation='softmax'))
# Compile the model
model2.compile(loss='categorical_crossentropy', optimizer='adam', metrics=
['accuracy'])
# Fit model
model2.fit(X, y, validation_split=0.33, epochs=30, callbacks=
[early_stopping_monitor], verbose=False)
############################## Initiate model 3 ###############################
# Model 3 has 1 hidden layer that is 2/3 the size of the input layer plus the size of the output layer
model3 = Sequential()
# Add first layer
model3.add(Dense(units=n_cols, activation='relu', input_shape=(n_cols,)))
# Add hidden layer
model3.add(Dense(units=math.ceil((n_cols*(2/3))+2), activation='relu'))
# Add output layer
model3.add(Dense(units=2, activation='softmax'))
# Compile the model
model3.compile(loss='categorical_crossentropy', optimizer='adam', metrics=
['accuracy'])
# Fit model
model3.fit(X, y, validation_split=0.33, epochs=30, callbacks=
[early_stopping_monitor], verbose=False)
# Plot the models
plt.plot(model1.history['val_loss'], 'r', model2.history['val_loss'], 'b',
model3.history['val_loss'], 'g')
plt.xlabel('Epochs')
plt.ylabel('Validation score')
plt.show()
I have no problems with running any of my models, getting predicted probabilities, plotting ROC curves, or plotting PR curves. However, when I attempt to plot the three curves together I am getting an error from this area of my code:
model1.history['val_loss']
TypeError: 'History' object is not subscriptable
Does anyone have experience with this type of error and can lead me to what I am doing wrong?
Thank you in advance.
Call to model.fit() returns a History object that has a member history, which is of type dict.
So you can replace :
model2.fit(X, y, validation_split=0.33, epochs=30, callbacks=
[early_stopping_monitor], verbose=False)
with
history2 = model2.fit(X, y, validation_split=0.33, epochs=30, callbacks=
[early_stopping_monitor], verbose=False)
Similarly for other models.
and then you can use :
plt.plot(history1.history['val_loss'], 'r', history2.history['val_loss'], 'b',
history3.history['val_loss'], 'g')