Update bcpModel_Sequetail.py

bcpModel_Sequetail.py

@@ -4,8 +4,11 @@
 # In[13]:
 
 
+#add the tensorflow package
+
 import tensorflow as tf
 
+#assign values to parameters
 mnist = tf.keras.datasets.mnist
 
 (x_train, y_train), (x_test, y_test) = mnist.load_data()
@@ -13,6 +16,8 @@ mnist = tf.keras.datasets.mnist
 x_train = tf.keras.utils.normalize(x_train, axis=1)
 x_test = tf.keras.utils.normalize(x_test, axis=1)
 
+#convential neural network archtecture
+#change the parameters- add4 sequentail layers and chenge epochs to 40
 model = tf.keras.models.Sequential()
 model.add(tf.keras.layers.Flatten())
 model.add(tf.keras.layers.Dense(128, activation=tf.nn.relu))
@@ -20,12 +25,12 @@ model.add(tf.keras.layers.Dense(128, activation=tf.nn.relu))
 model.add(tf.keras.layers.Dense(10, activation=tf.nn.softmax))
 model.compile(optimizer='adam',loss='sparse_categorical_crossentropy',
                 metrics=['accuracy'])
-model.fit(x_train, y_train, epochs=3)
+model.fit(x_train, y_train, epochs=40)
 
 
 # In[14]:
 
-
+#genrare accuracy level
 val_loss, val_acc = model.evaluate(x_test, y_test)
 print(val_loss,val_acc)
 
@@ -34,7 +39,7 @@ print(val_loss,val_acc)
 
 # In[11]:
 
-
+#view plot diagram
 import matplotlib.pyplot as plt
 
 
@@ -45,7 +50,7 @@ print(x_train[0])
 
 # In[36]:
 
-
+#save the model
 model.save("num_reader.model")