Update app.py

app.py

@@ -1,3 +1,33 @@
+import numpy as np
+import matplotlib.pyplot as plt
+from tensorflow.keras.datasets import mnist
+from tensorflow import keras
+import keras.backend as K
+from tensorflow.keras.layers import Dense, Flatten, Reshape, Input, Lambda, BatchNormalization, Dropout
+ 
+(x_train, y_train), (x_test, y_test) = mnist.load_data()
+ 
+x_train = x_train / 255
+x_test = x_test / 255
+
+y_train = keras.utils.to_categorical(y_train, 10)
+
+input_img = Input((28, 28))
+x = Flatten()(input_img)
+x = Dense(256, activation='relu')(x)
+x = Dense(128, activation='relu')(x)
+x = Dense(64, activation='relu')(x)
+Classif = Dense(10, activation='softmax')(x)
+
+model = keras.Model(input_img, Classif)
+
+model.compile(optimizer='adam', loss='categorical_crossentropy')
+
+model.fit(x_train, y_train, epochs=5, batch_size=30, shuffle=True)
+
+
+
+
 import gradio as gr
 import numpy as np