model code

Skin cancer detection/skin_cancer_coreml_model/keras_to_coreml_converter.py

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+import coremltools
+import h5py
+scale = 1/255.
+output_labels = ['benign', 'malignant']
+coreml_model = coremltools.converters.keras.convert('/Users/ted/Documents/skin_cancer_edge/skin_cancer_coreml_27_shared_folder/weights.best.from_scratch.6.hdf5')
+
+coreml_model.author = ''   
+coreml_model.short_description = 'Model to classify skin cancer'      
+print(coreml_model)
+coreml_model.save('/Users/ted/Documents/skin_cancer_edge/skin_cancer_coreml_27_shared_folder/weights_skin_cancer.mlmodel')

Skin cancer detection/skin_cancer_coreml_model/test_skin_cancer_app.py

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+from sklearn.datasets import load_files       
+from keras.utils import np_utils
+import numpy as np
+from glob import glob
+import keras
+from keras.preprocessing import image                  
+from tqdm import tqdm
+from keras.models import load_model
+from PIL import ImageFile                            
+ImageFile.LOAD_TRUNCATED_IMAGES = True    
+
+# define function to load train, test, and validation datasets
+def load_dataset(path):
+    data = load_files(path)
+    condition_files = np.array(data['filenames'])
+    condition_targets = np_utils.to_categorical(np.array(data['target']), 2)
+    return condition_files, condition_targets
+
+def path_to_tensor(img_path):
+    # loads RGB image as PIL.Image.Image type
+    img = image.load_img(img_path, target_size=(224, 224))
+    # convert PIL.Image.Image type to 3D tensor with shape (224, 224, 3)
+    x = image.img_to_array(img)
+    # convert 3D tensor to 4D tensor with shape (1, 224, 224, 3) and return 4D tensor
+    return np.expand_dims(x, axis=0)
+
+
+def condition(img_path):
+    label = 'None'
+    score = 0
+    test_tensor = path_to_tensor(img_path).astype('float32')/255
+    # obtain predicted vector
+    condition_prediction = model.predict(test_tensor)
+    print condition_prediction
+    benign, malignant = condition_prediction.max(axis=0)
+    if benign > malignant:
+    	label = 'benign'
+	score = benign
+    else:
+        label = 'malignant'
+	score = malignant
+    return label, score
+
+### Test a malignant image 
+test_image = '/Users/ted/Documents/skin_cancer_edge/skin_cancer_coreml_27_shared_folder/test_images/malignant/54e755f6bae47850e86cdfef.jpg'
+model = load_model('/Users/ted/Documents/skin_cancer_edge/skin_cancer_coreml_27_shared_folder/weights.best.from_scratch.6.hdf5')
+label, score = condition(test_image)
+print label, score
+
+### Test a benign image 
+test_image = '/Users/ted/Documents/skin_cancer_edge/skin_cancer_coreml_27_shared_folder/test_images/benign/57eea3479fc3c12a89bb51fa.jpg'
+model = load_model('/Users/ted/Documents/skin_cancer_edge/skin_cancer_coreml_27_shared_folder/weights.best.from_scratch.6.hdf5')
+label, score = condition(test_image)
+print label, score
+
+
+

Skin cancer detection/skin_cancer_coreml_model/train_skin_cancer_app.py

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+
+# coding: utf-8
+
+# In[1]:
+
+from sklearn.datasets import load_files       
+from keras.utils import np_utils
+import numpy as np
+from glob import glob
+import keras
+
+# define function to load train, test, and validation datasets
+def load_dataset(path):
+    data = load_files(path)
+    condition_files = np.array(data['filenames'])
+    condition_targets = np_utils.to_categorical(np.array(data['target']), 2)
+    return condition_files, condition_targets
+
+# load train, test, and validation datasets
+train_files, train_targets = load_dataset('/data/Keras_Transfer_Learning/ISIC_Skin_Cancer/data/train')
+valid_files, valid_targets = load_dataset('/data/Keras_Transfer_Learning/ISIC_Skin_Cancer/data/validation')
+test_files, test_targets = load_dataset('/data/Keras_Transfer_Learning/ISIC_Skin_Cancer/data/test')
+
+# load list of dog names
+condition_names = [item[58:-1] for item in sorted(glob("/data/Keras_Transfer_Learning/ISIC_Skin_Cancer/data/train/*/"))]
+print (condition_names)
+# print statistics about the dataset
+print('There are %d total categories.' % len(condition_names))
+print('There are %s total images.\n' % len(np.hstack([train_files, valid_files, test_files])))
+print('There are %d training images.' % len(train_files))
+print('There are %d validation images.' % len(valid_files))
+print('There are %d test images.'% len(test_files))
+
+
+# In[2]:
+
+from keras.preprocessing import image                  
+from tqdm import tqdm
+
+def path_to_tensor(img_path):
+    # loads RGB image as PIL.Image.Image type
+    img = image.load_img(img_path, target_size=(224, 224))
+    # convert PIL.Image.Image type to 3D tensor with shape (224, 224, 3)
+    x = image.img_to_array(img)
+    # convert 3D tensor to 4D tensor with shape (1, 224, 224, 3) and return 4D tensor
+    return np.expand_dims(x, axis=0)
+
+def paths_to_tensor(img_paths):
+    list_of_tensors = [path_to_tensor(img_path) for img_path in tqdm(img_paths)]
+    return np.vstack(list_of_tensors)
+
+
+# ---
+# <a id='step3'></a>
+# ## Step 3: Create a CNN to Classify Dog Breeds (from Scratch)
+# 
+# ### Pre-process the Data
+# 
+# We rescale the images by dividing every pixel in every image by 255.
+
+# In[3]:
+
+from PIL import ImageFile                            
+ImageFile.LOAD_TRUNCATED_IMAGES = True                 
+
+# pre-process the data for Keras
+train_tensors = paths_to_tensor(train_files).astype('float32')/255
+valid_tensors = paths_to_tensor(valid_files).astype('float32')/255
+test_tensors = paths_to_tensor(test_files).astype('float32')/255
+
+
+# ### (IMPLEMENTATION) Model Architecture
+# 
+
+# In[4]:
+from keras.layers import Conv2D, MaxPooling2D, GlobalAveragePooling2D
+from keras.layers import Dropout, Flatten, Dense
+from keras.models import Sequential
+
+model = Sequential()
+model.add(Conv2D(32, kernel_size=(3, 3),
+                 activation='relu',
+                 input_shape=(224, 224, 3)))
+model.add(Conv2D(64, (3, 3), activation='relu'))
+model.add(MaxPooling2D(pool_size=(2, 2)))
+model.add(Dropout(0.25))
+model.add(Flatten())
+model.add(Dense(128, activation='relu'))
+model.add(Dropout(0.5))
+model.add(Dense(2, activation='sigmoid'))
+
+model.summary()
+
+
+# ### Compile the Model
+
+# In[ ]:
+#opt = keras.optimizers.Adadelta()
+opt = keras.optimizers.Adam(lr=0.0001, beta_1=0.9, beta_2=0.999, epsilon=1e-08, decay=0.0)
+model.compile(optimizer=opt, loss='binary_crossentropy', metrics=['accuracy'])
+
+
+# ### Train the Model
+# 
+
+# In[ ]:
+
+from keras.callbacks import ModelCheckpoint  
+
+### TODO: specify the number of epochs that you would like to use to train the model.
+
+epochs = 15
+
+checkpointer = ModelCheckpoint(filepath='weights.best.from_scratch.6.hdf5', 
+                               verbose=1, save_best_only=True)
+
+model.fit(train_tensors, train_targets, 
+          validation_data=(valid_tensors, valid_targets),
+          epochs=epochs, batch_size=10, callbacks=[checkpointer], verbose=1)
+
+# ### Load the Model with the Best Validation Loss
+
+# In[5]:
+
+model.load_weights('weights.best.from_scratch.6.hdf5')
+
+
+# ### Test the Model
+# 
+
+# In[6]:
+
+# get index of predicted dog breed for each image in test set
+condition_predictions = [np.argmax(model.predict(np.expand_dims(tensor, axis=0))) for tensor in test_tensors]
+
+# report test accuracy
+test_accuracy = 100*np.sum(np.array(condition_predictions)==np.argmax(test_targets, axis=1))/len(condition_predictions)
+print('Test accuracy: %.4f%%' % test_accuracy)
+