可视化

main.py

@@ -1,5 +1,8 @@
+import os
 import tensorflow as tf
+import matplotlib.pyplot as plt
 from tensorflow import keras
+import numpy as np
 config = tf.compat.v1.ConfigProto(gpu_options=tf.compat.v1.GPUOptions(allow_growth=True))
 sess = tf.compat.v1.Session(config=config)
 
@@ -19,13 +22,39 @@ model.add(keras.layers.MaxPooling2D(2,2))
 
 model.add(keras.layers.Flatten())
 model.add(keras.layers.Dense(128, activation = tf.nn.relu))
-model.add(keras.layers.Dense(36, activation = tf.nn.softmax))
+model.add(keras.layers.Dense(10, activation = tf.nn.softmax))
+
+checkpoint_path = "training_1/cp.ckpt"
+checkpoint_dir = os.path.dirname(checkpoint_path)
+
+cp_callback = tf.keras.callbacks.ModelCheckpoint(filepath=checkpoint_path,
+                                                 save_weights_only=True,
+                                                 verbose=1)
 
 train_images_scaled = train_images/255
 model.compile(optimizer = 'adam', loss = tf.losses.sparse_categorical_crossentropy, metrics = ['accuracy'])
 
-history = model.fit(train_images_scaled.reshape(-1, 28, 28 ,1), train_labels, epochs = 8)
+history = model.fit(
+                        train_images_scaled.reshape(-1, 28, 28 ,1),
+                        train_labels,
+                        epochs = 8,
+                        validation_data=(test_images.reshape(-1, 28, 28 ,1), test_labels),
+                        callbacks=[cp_callback]
+                    )
 
 results = model.evaluate(test_images.reshape(-1, 28, 28 ,1), test_labels)
 
+testShow = test_labels[:100]
+
+pred = model.predict(test_images.reshape(-1, 28, 28 ,1))
+predict = []
+for item in pred:
+    predict.append(np.argmax(item))
 
+plt.figure()
+plt.title('Conv Predict')
+plt.ylabel('number')
+plt.plot( range( testShow.size ), predict[:100], label='predict')
+plt.plot( range( testShow.size ), testShow, label='result')
+plt.legend()
+plt.show()