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Let us train the model on the train data.
Note:
plt.gca()
is used to get a reference to the current axes, if you need to change the limits on the y-axis, for example.Use model.fit
to train the model. We shall pass the X_train, y_train, epochs=30, validation_data=(X_valid, y_valid)
as input parameters to the method.
history = model.fit(X_train, y_train, epochs=30,
validation_data=(X_valid, y_valid))
This might take some time, so go and have a sip of water if you like.
Let us now print the parameter history of the history
.
history.params
Let us print the name of the first hidden layer:
hidden1 = model.layers[1]
print(hidden1.name)
Let us use get_weights()
function to see the trained weights
and biases
of the hidden1
.
weights, biases = hidden1.get_weights() # getting the weights and biases
print(weights.shape, weights)
print(biases)
Let us visualize the model training history. Here, history.history
is a dictionary containing information about the accuracy and loss measures through the epochs.
Let us store this dictionary as a pandas dataframe and plot the values.
import pandas as pd
pd.DataFrame(history.history).plot(figsize=(8, 5))
plt.grid(True)
plt.gca().set_ylim(0, 1) # setting limits for y-axis
plt.show()
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