tworzenie modelu

siec.py

@@ -0,0 +1,45 @@
+import numpy
+from keras.datasets import mnist
+from keras.layers import Conv2D, MaxPooling2D
+from keras.layers import Dense, Dropout, Flatten
+from keras.models import Sequential
+from keras.utils import np_utils
+from keras_preprocessing.image import load_img, img_to_array
+
+
+img_rows, img_cols = 28, 28
+input_shape = (img_rows, img_cols, 1)
+
+producent = []
+def imageClass(model):
+    model.add(Conv2D(75, kernel_size=(5, 5),
+                     activation='relu',
+                     input_shape=input_shape))
+    model.add(MaxPooling2D(pool_size=(2, 2)))
+    model.add(Dropout(0.2))
+    model.add(Conv2D(100, (5, 5), activation='relu'))
+    model.add(MaxPooling2D(pool_size=(2, 2)))
+    model.add(Dropout(0.2))
+    model.add(Flatten())
+    model.add(Dense(500, activation='relu'))
+    model.add(Dropout(0.5))
+    model.add(Dense(10, activation='softmax'))
+
+    model.compile(loss="categorical_crossentropy", optimizer="adam", metrics=["accuracy"])
+
+nmodel = Sequential()
+imageClass(nmodel)
+nmodel.load_weights('model_weights.h5')
+
+def imgSkan():
+    img_width, img_height = 28, 28
+    new_image = load_img('cyfra.png', target_size=(img_width, img_height), color_mode = "grayscale")
+    new_image = img_to_array(new_image)
+    new_image = new_image.reshape((1,) + new_image.shape)
+
+    prediction = nmodel.predict(new_image)
+    prediction = numpy.argmax(prediction)
+    print("Producent:", prediction)
+    producent.append(prediction)
+
+imgSkan()