New lstm examples

convolution-demo.py

@@ -14,7 +14,7 @@
 image = io.imread('dog.jpg', as_grey=True)
 
 kernel = [[0.0, 0.5, 0.0],
-          [0.5, 0.0, -0.5],
+          [-0.0, 0.0, 0.0],
           [0.0, -0.5, 0.0]]
 
 new_image = convolve2d(image, kernel)

dogcat-1.py

@@ -17,8 +17,8 @@
 # dimensions of our images.
 img_width, img_height = 150, 150
 
-train_data_dir = 'dogcat-data/train'
-validation_data_dir = 'dogcat-data/validation'
+train_data_dir = 'dogcat-data-small/train'
+validation_data_dir = 'dogcat-data-small/validation'
 nb_train_samples = 2000
 nb_validation_samples = 2000
 epochs = 50
@@ -45,7 +45,8 @@
 model.add(Dropout(0.5))
 model.add(Dense(1))
 model.add(Activation('sigmoid'))
-
+model.summary()
+exit()
 model.compile(loss='binary_crossentropy', optimizer='sgd',
               metrics=['accuracy'])
 

dogcat-generator.py

@@ -23,9 +23,9 @@
         horizontal_flip=True,
         fill_mode='nearest')
 
-img = load_img('data/train/cats/cat.0.jpg')  # this is a PIL image
-x = img_to_array(img)  # this is a Numpy array with shape (3, 150, 150)
-x = x.reshape((1,) + x.shape)  # this is a Numpy array with shape (1, 3, 150, 150)
+#img = load_img('data/train/cats/cat.0.jpg')  # this is a PIL image
+#x = img_to_array(img)  # this is a Numpy array with shape (3, 150, 150)
+#x = x.reshape((1,) + x.shape)  # this is a Numpy array with shape (1, 3, 150, 150)
 
 # the .flow() command below generates batches of randomly transformed images
 # and saves the results to the `preview/` directory