reg_model = create_model()
save_graph_plot(reg_model, project_paths["plots"] + "/reg_model.ps")
save_graph_json(reg_model, project_paths["plots"] + "/reg_model.json")
reg_model_hist = reg_model.fit(
train_images,
train_labels,
epochs=epochs,
validation_data=(test_images, test_labels),
callbacks=[csv_logger, callback_save_model_reg, callback_weights_reg])
model_list.append(reg_model_hist)
model_name_list.append("Regular model ")
# Pred Model
# pred_model = create_model()
pred_model = tf.keras.models.load_model(restore_path)
pred_model_hist = pred_model.fit(train_images,
train_labels,
epochs=epochs - TotalSkips - 1,
initial_epoch=FirstSkip,
validation_data=(test_images, test_labels),
callbacks=[csv_logger, callback_weights_pred])
model_list.append(pred_model_hist)
model_name_list.append("Pred Model ")
drawPlot_acc_loss(model_list, model_name_list, project_paths["plots"])
optimizer='adam',
loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
metrics=['accuracy'])
reg_model_hist = reg_model.fit(train_images,
train_labels,
epochs=10,
validation_data=(test_images, test_labels))
model_list.append(reg_model_hist)
model_name_list.append("reg_model")
norm_model = tf.keras.Sequential([
tf.keras.layers.Flatten(input_shape=(10, 10)),
tf.keras.layers.Dense(15, activation='relu'),
tf.keras.layers.Dense(10)
])
norm_model.compile(
optimizer='adam',
loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
metrics=['accuracy'])
norm_model_hist = norm_model.fit(train_images,
train_labels,
epochs=10,
validation_data=(test_images, test_labels))
model_list.append(norm_model_hist)
model_name_list.append("norm_model")
#drawPlot_acc_loss(model_list, model_name_list,project_paths["plots"])
drawPlot_acc_loss(model_list, model_name_list,
'/home/sap/IdeaProjects/XAI/April/plots')
