rem mat

run.py

@@ -1,6 +1,6 @@
 import tensorflow as tf
 from tensorflow import keras
-import matplotlib.pyplot as plt
+
 import numpy as np
 import os
 # Version Information
@@ -27,3 +27,53 @@ print(tf.test.is_built_with_cuda())
 (X_train, y_train), (X_test,y_test) = tf.keras.datasets.cifar10.load_data()
 
 print(X_train.shape,y_train.shape)
+
+classes = ["airplane","automobile","bird","cat","deer","dog","frog","horse","ship","truck"]
+
+print(classes[y_train[3][0]])
+
+print("pre processing: scale images")
+
+X_train_scaled = X_train / 255
+X_test_scaled = X_test / 255
+
+y_train_categorical = keras.utils.to_categorical(
+    y_train, num_classes=10, dtype='float32'
+)
+y_test_categorical = keras.utils.to_categorical(
+    y_test, num_classes=10, dtype='float32'
+)
+
+print("model build")
+
+model = keras.Sequential([
+        keras.layers.Flatten(input_shape=(32,32,3)),
+        keras.layers.Dense(300, activation='relu'),
+        keras.layers.Dense(100, activation='relu'),
+        keras.layers.Dense(10, activation='sigmoid')    
+    ])
+
+model.compile(optimizer='SGD',
+              loss='categorical_crossentropy',
+              metrics=['accuracy'])
+
+model.fit(X_train_scaled, y_train_categorical, epochs=1)
+
+def get_model():
+    model = keras.Sequential([
+            keras.layers.Flatten(input_shape=(32,32,3)),
+            keras.layers.Dense(3000, activation='relu'),
+            keras.layers.Dense(1000, activation='relu'),
+            keras.layers.Dense(10, activation='sigmoid')    
+        ])
+
+    model.compile(optimizer='SGD',
+                  loss='categorical_crossentropy',
+                  metrics=['accuracy'])
+    return model
+
+with tf.device('/GPU:0'):
+    cpu_model = get_model()
+    cpu_model.fit(X_train_scaled, y_train_categorical, epochs=10)
+    
+print("done")