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visulization.py

+import matplotlib.pyplot as plt
+import numpy as np
+
+
+def plot_model_history(model_history):
+    fig, axs = plt.subplots(1, 2, figsize=(15, 5))
+    # summarize history for accuracy
+    axs[0].plot(range(1, len(model_history.history['acc']) + 1),
+                model_history.history['acc'])
+    axs[0].plot(range(1, len(model_history.history['val_acc']) + 1),
+                model_history.history['val_acc'])
+    axs[0].set_title('Model Accuracy')
+    axs[0].set_ylabel('Accuracy')
+    axs[0].set_xlabel('Epoch')
+    axs[0].set_xticks(np.arange(1, len(model_history.history[
+                      'acc']) + 1), len(model_history.history['acc']) / 10)
+    axs[0].legend(['train', 'val'], loc='best')
+    # summarize history for loss
+    axs[1].plot(range(1, len(model_history.history['loss']) + 1),
+                model_history.history['loss'])
+    axs[1].plot(range(1, len(model_history.history['val_loss']) + 1),
+                model_history.history['val_loss'])
+    axs[1].set_title('Model Loss')
+    axs[1].set_ylabel('Loss')
+    axs[1].set_xlabel('Epoch')
+    axs[1].set_xticks(np.arange(1, len(model_history.history[
+                      'loss']) + 1), len(model_history.history['loss']) / 10)
+    axs[1].legend(['train', 'val'], loc='best')
+    plt.show()