atualização de versão

cnn.py

@@ -22,7 +22,8 @@
 classifier = Sequential()
 
 # Step 1 - Convolution
-classifier.add(Conv2D(32, (3, 3), input_shape = (64, 64, 3), activation = 'relu'))
+classifier.add(
+        Conv2D(32, (3, 3), input_shape = (64, 64, 3), activation = 'relu'))
 
 # Step 2 - Pooling
 classifier.add(MaxPooling2D(pool_size = (2, 2)))
@@ -66,4 +67,13 @@
                          steps_per_epoch = 8000,
                          epochs = 25,
                          validation_data = test_set,
-                         validation_steps = 2000)
+                         validation_steps = 2000)
+
+
+
+
+
+
+
+
+

cnn_homework_solution.py

@@ -0,0 +1,83 @@
+# Convolutional Neural Network
+
+# Installing Theano
+# pip install --upgrade --no-deps git+git://github.com/Theano/Theano.git
+
+# Installing Tensorflow
+# pip install tensorflow
+
+# Installing Keras
+# pip install --upgrade keras
+
+# Part 1 - Building the CNN
+
+# Importing the Keras libraries and packages
+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 - Pooling
+classifier.add(MaxPooling2D(pool_size = (2, 2)))
+
+# Adding a 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(units = 128, activation = 'relu'))
+classifier.add(Dense(units = 1, activation = 'sigmoid'))
+
+# Compiling the CNN
+classifier.compile(optimizer = 'adam', loss = 'binary_crossentropy', metrics = ['accuracy'])
+
+# Part 2 - Fitting the CNN to the images
+
+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('dataset/training_set',
+                                                 target_size = (64, 64),
+                                                 batch_size = 32,
+                                                 class_mode = 'binary')
+
+test_set = test_datagen.flow_from_directory('dataset/test_set',
+                                            target_size = (64, 64),
+                                            batch_size = 32,
+                                            class_mode = 'binary')
+
+classifier.fit_generator(training_set,
+                         steps_per_epoch = 8000,
+                         epochs = 25,
+                         validation_data = test_set,
+                         validation_steps = 2000)
+
+# Part 3 - Making new predictions
+
+import numpy as np
+from keras.preprocessing import image
+test_image = image.load_img('dataset/single_prediction/cat_or_dog_1.jpg', target_size = (64, 64))
+test_image = image.img_to_array(test_image)
+test_image = np.expand_dims(test_image, axis = 0)
+result = classifier.predict(test_image)
+training_set.class_indices
+if result[0][0] == 1:
+    prediction = 'dog'
+else:
+    prediction = 'cat'