Part 2 added

Author: ahmedfgad

3.jpg

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+import flask
+import werkzeug
+import keras.models
+import numpy
+import scipy.misc
+
+app = flask.Flask(__name__)
+
+@app.route('/', methods = ['GET', 'POST'])
+def handle_request():
+    imagefile = flask.request.files['image']
+    filename = werkzeug.utils.secure_filename(imagefile.filename)
+    print("\nReceived image File name : " + imagefile.filename)
+    imagefile.save(filename)
+    
+    img = scipy.misc.imread(filename, mode="L")
+    img = img.reshape(784)
+    loaded_model = keras.models.load_model('model.h5')
+    predicted_label = loaded_model.predict_classes(numpy.array([img]))[0]
+    print(predicted_label)
+
+    return str(predicted_label)
+
+app.run(host="0.0.0.0", port=5000, debug=True)

6.jpg

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+import keras.datasets.mnist
+import keras.models
+import keras.layers
+import keras.optimizers
+import numpy
+
+batch_size = 128
+num_classes = 10
+epochs = 20
+
+# the data, split between train and test sets
+(x_train, y_train), (x_test, y_test) = keras.datasets.mnist.load_data()
+print(x_train.shape) # (60000, 28, 28)
+print(y_train.shape) # (60000,)
+
+print(x_test.shape) # (10000, 28, 28)
+print(y_test.shape) # (10000,)
+
+x_train = x_train.reshape(60000, 784)
+x_test = x_test.reshape(10000, 784)
+x_train = x_train.astype('float32') / 255
+x_test = x_test.astype('float32') / 255
+
+y_train = keras.utils.to_categorical(y_train, num_classes) # (60000, 10)
+y_test = keras.utils.to_categorical(y_test, num_classes) # (10000, 10)
+
+model = keras.models.Sequential()
+model.add(keras.layers.Dense(512, activation='relu', input_shape=(784,)))
+model.add(keras.layers.Dropout(0.2))
+model.add(keras.layers.Dense(512, activation='relu'))
+model.add(keras.layers.Dropout(0.2))
+model.add(keras.layers.Dense(num_classes, activation='softmax'))
+
+model.summary()
+
+model.compile(loss='categorical_crossentropy',
+              optimizer=keras.optimizers.RMSprop(),
+              metrics=['accuracy'])
+
+history = model.fit(x_train, y_train,
+                    batch_size=batch_size,
+                    epochs=epochs,
+                    verbose=1,
+                    validation_data=(x_test, y_test))
+
+score = model.evaluate(x_test, y_test, verbose=0)
+print('Test loss:', score[0])
+print('Test accuracy:', score[1])
+
+model.save("model.h5")
+
+loaded_model = keras.models.load_model('model.h5')
+predicted_label = loaded_model.predict_classes(numpy.array([x_test[0, :]]))[0]
+print(predicted_label)