Update app.py

app.py

@@ -2,11 +2,14 @@ from keras.models import load_model
 import numpy as np
 
 # Load the saved model
-model = load_model('model.h5')
+# model = load_model('model.h5')
+
+# predicted_image = model.predict(np.expand_dims(input_image, axis=0))
 
-predicted_image = model.predict(np.expand_dims(input_image, axis=0))
 
 import tensorflow as tf
+from PIL import Image
+import numpy as np
 from tensorflow.keras.datasets import cifar10
 from tensorflow.keras.models import Sequential
 from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense, Dropout
@@ -25,12 +28,61 @@ x_test = x_test.astype('float32') / 255.0
 y_train = to_categorical(y_train, num_classes=10)
 y_test = to_categorical(y_test, num_classes=10)
 
-predicted = model.predict(x_test)
 
-np.argmax(predicted, axis = 1)
 
-from PIL import Image
-import numpy as np
+
+
+
+# predicted = model.predict(x_test)
+
+# np.argmax(predicted, axis = 1)
+
+
+model = Sequential([
+    # First convolutional layer
+    Conv2D(96, (11, 11), strides=(1, 1), activation='relu', input_shape=(32, 32, 3)),
+    MaxPooling2D(pool_size=(3, 3), strides=(2, 2)),
+    # Second convolutional layer
+    Conv2D(256, (5, 5), padding='same', activation='relu'),
+    MaxPooling2D(pool_size=(3, 3), strides=(2, 2)),
+    # Third convolutional layer
+    Conv2D(384, (3, 3), padding='same', activation='relu'),
+    # Fourth convolutional layer
+    Conv2D(384, (3, 3), padding='same', activation='relu'),
+    # Fifth convolutional layer
+    Conv2D(256, (3, 3), padding='same', activation='relu'),
+    MaxPooling2D(pool_size=(3, 3), strides=(2, 2)),
+    # Flatten the convolutional layers output for fully connected layers
+    Flatten(),
+    # First fully connected layer
+    Dense(4096, activation='relu'),
+    Dropout(0.5),
+    # Second fully connected layer
+    Dense(4096, activation='relu'),
+    Dropout(0.5),
+    # Output layer
+    Dense(10, activation='softmax')
+])
+
+# Compile the model with a lower learning rate
+optimizer = Adam(learning_rate=0.0001)
+model.compile(optimizer=optimizer,
+              loss='categorical_crossentropy',
+              metrics=['accuracy'])
+
+# Data augmentation
+datagen = ImageDataGenerator(
+    rotation_range=15,
+    width_shift_range=0.1,
+    height_shift_range=0.1,
+    horizontal_flip=True,
+)
+
+datagen.fit(x_train)
+
+# Train the model with data augmentation
+model.fit(datagen.flow(x_train, y_train, batch_size=128), epochs=25, validation_data=(x_test, y_test))
+
 
 classes = {
     0 : 'Airplane',