Megha submission: Submission

meghat525@gmail.com/Submission/Fruits360.py

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+#CNN
+#Part 1-Building the CNN
+from keras.models import Sequential
+from keras.layers import Conv2D
+from keras.layers import MaxPooling2D
+from keras.layers import Flatten
+from keras.layers import Dense
+#Initialising the CNN
+classifier=Sequential()
+#Step 1-Convolution
+classifier.add(Conv2D(32,(3,3),input_shape=(64,64,3),activation="relu"))
+#Step 2- Max Pooling
+classifier.add(MaxPooling2D(pool_size=(2,2)))
+#Adding second convolutional layer
+classifier.add(Conv2D(32,(3,3),activation="relu"))
+classifier.add(MaxPooling2D(pool_size=(2,2)))
+#Step 3-Flattening
+classifier.add(Flatten())
+#Step 4-Full Connection
+classifier.add(Dense(activation="relu",units=128))
+classifier.add(Dense(activation="softmax",units=114))
+#Compiling the ANN
+classifier.compile(optimizer="adam",loss="categorical_crossentropy",metrics=["accuracy"])
+#Fitting the dataset
+from keras.preprocessing.image import ImageDataGenerator
+train_datagen = ImageDataGenerator(
+        rescale=1./255,
+        shear_range=0.2,
+        zoom_range=0.2,
+        horizontal_flip=True)
+
+test_datagen = ImageDataGenerator(rescale=1./255)
+
+training_set = train_datagen.flow_from_directory(
+        'Training', #Directory
+        target_size=(64, 64), #Same as that in input_shape
+        batch_size=32,
+        class_mode='categorical')
+
+test_set = test_datagen.flow_from_directory(
+        'Test',
+        target_size=(64, 64),
+        batch_size=50,
+        class_mode='categorical')
+
+classifier.fit_generator(
+        training_set,
+        steps_per_epoch=57276, #No. of images in training set
+        epochs=7, #No. of epochs
+        validation_data=test_set,
+        validation_steps=19548) #No. of images in test set