I am trying to develop a small feature that plots dynamically the loss or accuracy during the training of a Tensorflow model. I basically plot the history of accuracies at the end of each batch processing for each epoch (the code still needs some corrections but it works correctly for now). I have a small problem, as I run the following code in a jupyter notebook cell. I have the desired behavior, with a plot that evolves dynamically. However at the end of the training the final plot is duplicated for some reason and I can't figure out why it is the case. from IPython.display import display, clear_output import tensorflow as tf from tensorflow.keras.models import Sequential import numpy as np import matplotlib.pyplot as plt class CustomCallback(tf.keras.callbacks.Callback): def on_train_begin(self, logs=None): self.epoch = 0 # Initialize the epoch counter self.accuracies = [] self.fig, self.ax = plt.subplots() self.line, = self.ax.plot([], []) self.ax.set_xlim(0, 30) self.ax.set_ylim(0, 1) self.displayed = False display(self.fig) def on_epoch_begin(self, epoch, logs=None): self.epoch = epoch # Update the current epoch at the beginning of each epoch def on_train_batch_end(self, batch, logs=None): accuracy = logs['accuracy'] self.accuracies.append(accuracy) self.line.set_data(range(1, len(self.accuracies) + 1), self.accuracies) self.ax.relim() self.ax.autoscale_view() clear_output(wait=True) display(self.fig) custom_callback = CustomCallback() model = Sequential() model.add(tf.keras.layers.Dense(units=16, activation='relu')) model.add(tf.keras.layers.Dropout(rate=0.35)) model.add(tf.keras.layers.Dense(units=1, activation='tanh')) model.compile(optimizer=tf.keras.optimizers.Adam(), loss="binary_crossentropy", metrics=["accuracy"]) X = np.random.randn(10**2, 10**4) y = np.random.randint(2, size=10**2) abc = model.fit(X, y, epochs=7, batch_size=32, validation_split=0.025, verbose=False, callbacks=[custom_callback]) Continue reading...
