<gh_stars>0
#!/usr/bin/env python
# coding: utf-8

# # Import Libraries

# In[23]:


#Importing and Installing Required Modules and Libraries
get_ipython().system('pip install tensorflow')
get_ipython().system('pip install basic_image_eda')
import os
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.colors as mcolors
import seaborn as sns
sns.set_theme(style="whitegrid")
import cv2
import sys
from re import sub
import tensorflow as tf
from tensorflow.keras import metrics
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import BatchNormalization
from tensorflow.keras.layers import Conv2D
from tensorflow.keras.layers import MaxPooling2D
from tensorflow.keras.layers import Activation, Flatten, Dropout, Dense
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.preprocessing import image
from tensorflow.keras import models
from tensorflow.keras.preprocessing import image_dataset_from_directory


# # Load Dataset

# In[24]:


data_dir = r"Downloads/plantvillage-dataset/color/"


# # Sample Image

# In[25]:


img = plt.imread(data_dir+"Apple___Apple_scab/00075aa8-d81a-4184-8541-b692b78d398a___FREC_Scab 3335.JPG")
plt.imshow(img)


# # Exploratory Data Analysis

# In[26]:


category_count = []

for root, dirs, files in os.walk(data_dir):
    for dir_path in dirs:
        category_count.append((dir_path, len(os.listdir(root+os.sep+dir_path))))
count_df = pd.DataFrame(category_count, columns=['Category', 'Count'])
count_df.head(10)


# In[27]:


total_images = count_df['Count'].sum()
total_images


# # Data Visualization by Category

# In[28]:


count_df = count_df.sort_values(by='Count', ascending=False)
plt.figure(figsize=(20,20))
sns.barplot(x="Count", y="Category", data=count_df)
plt.plot()


# In[29]:


healthy_images_count = count_df[count_df['Category'].str.endswith("healthy")]['Count'].sum()
healthy_images_count


# In[30]:


disease_images_count = total_images - healthy_images_count
disease_images_count


# In[31]:


temp_df = pd.DataFrame(data=[("Unhealthy", disease_images_count), ("Healthy", healthy_images_count)], columns=['Category', 'Count'])
sns.barplot(y="Count",x="Category", data=temp_df)
plt.plot()


# # Data Augmentation and Pre-processing

# In[32]:


BATCH_SIZE = 32
IMG_SIZE = (240, 240)


# In[33]:


train_dataset = image_dataset_from_directory(data_dir,
                                             shuffle=True,
                                             label_mode = 'categorical',
                                             validation_split = 0.2,
                                             batch_size=BATCH_SIZE,
                                             seed = 42,
                                             subset = "training",
                                             image_size=IMG_SIZE)

validation_dataset = image_dataset_from_directory(data_dir,
                                             shuffle=True,
                                             label_mode = 'categorical',
                                             validation_split = 0.2,
                                             batch_size=BATCH_SIZE,
                                             seed = 42,
                                             subset = "validation",
                                             image_size=IMG_SIZE)


# In[34]:


class_names = train_dataset.class_names
num_classes = len(class_names)
for i in range(1, num_classes + 1):
    print(str(i) + ". ", class_names[i - 1])


# In[35]:


val_batches = tf.data.experimental.cardinality(validation_dataset)
test_dataset = validation_dataset.take(val_batches // 5)
validation_dataset = validation_dataset.skip(val_batches // 5)

print('Number of validation batches: %d' % tf.data.experimental.cardinality(validation_dataset))
print('Number of test batches: %d' % tf.data.experimental.cardinality(test_dataset))


# In[36]:


AUTOTUNE = tf.data.AUTOTUNE

train_dataset = train_dataset.prefetch(buffer_size=AUTOTUNE)
validation_dataset = validation_dataset.prefetch(buffer_size=AUTOTUNE)
test_dataset = test_dataset.prefetch(buffer_size=AUTOTUNE)


# In[37]:


# added augmentations
data_augmentation = tf.keras.Sequential([
  tf.keras.layers.experimental.preprocessing.RandomFlip('horizontal'),
  tf.keras.layers.experimental.preprocessing.RandomRotation(0.2),
])


# In[38]:


for image, _ in train_dataset.take(1):
  plt.figure(figsize=(10, 10))
  first_image = image[0]
  for i in range(9):
    ax = plt.subplot(3, 3, i + 1)
    augmented_image = data_augmentation(tf.expand_dims(first_image, 0))
    plt.imshow(augmented_image[0] / 255)
    plt.axis('off')


# ## Metrics and Plotting functions

# In[39]:


def plot_metrics(history):
  colors = ['b', 'g', 'r', 'c', 'm', 'y', 'k', 'w']
  metrics = ['loss', 'auc', 'precision', 'recall']
  plt.figure(figsize=(20,10))
  for n, metric in enumerate(metrics):
    name = metric.replace("_"," ").capitalize()
    plt.subplot(2,2,n+1)
    plt.plot(history.epoch, history.history[metric], color=colors[0], label='Train')
    plt.plot(history.epoch, history.history['val_'+metric],
             color=colors[0], linestyle="--", label='Val')
    plt.xlabel('Epoch')
    plt.ylabel(name)
    if metric == 'loss':
      plt.ylim([0, plt.ylim()[1]])
    elif metric == 'auc':
      plt.ylim([0.8,1])
    else:
      plt.ylim([0,1])

    plt.legend()


# In[40]:


METRICS = [
      metrics.TruePositives(name='tp'),
      metrics.FalsePositives(name='fp'),
      metrics.TrueNegatives(name='tn'),
      metrics.FalseNegatives(name='fn'), 
      metrics.CategoricalAccuracy(name='accuracy'),
      metrics.Precision(name='precision'),
      metrics.Recall(name='recall'),
      metrics.AUC(name='auc')
]


# ## Load and compile model

# In[41]:


IMG_SHAPE = IMG_SIZE + (3,)


# In[42]:


preprocess_input = tf.keras.applications.inception_resnet_v2.preprocess_input


# In[43]:


base_model = tf.keras.applications.InceptionResNetV2(
                                include_top=False,
                                weights="imagenet",
                                input_shape=IMG_SHAPE,
                            )


# In[44]:


image_batch, label_batch = next(iter(train_dataset))
feature_batch = base_model(image_batch)
print(feature_batch.shape)


# In[45]:


base_model.trainable = False


# In[46]:


global_average_layer = tf.keras.layers.GlobalAveragePooling2D()
feature_batch_average = global_average_layer(feature_batch)

print(feature_batch_average.shape)


# In[47]:


prediction_layer = tf.keras.layers.Dense(num_classes, activation="softmax")
prediction_batch = prediction_layer(feature_batch_average)
print(prediction_batch.shape)


# In[48]:


inputs = tf.keras.Input(shape=(240, 240, 3))
x = data_augmentation(inputs)
x = preprocess_input(x)
x = base_model(x, training=False)
x = global_average_layer(x)
x = tf.keras.layers.Dropout(0.2)(x)
outputs = prediction_layer(x)
model = tf.keras.Model(inputs, outputs)


# In[49]:


base_learning_rate = 0.001
model.compile(optimizer=tf.keras.optimizers.Adam(lr=base_learning_rate),
              loss=tf.keras.losses.CategoricalCrossentropy(from_logits=True),
              metrics=METRICS)


# In[50]:


model.summary()


# # Training, Validation and Testing

# Before tuning

# In[51]:


initial_epochs = 10


# In[52]:


history = model.fit(train_dataset,
                    epochs=initial_epochs,
                    validation_data=validation_dataset)


# After tunung

# In[53]:


base_model.trainable = True


# In[54]:


# Let's take a look to see how many layers are in the base model
print("Number of layers in the base model: ", len(base_model.layers))

# Fine-tune from this layer onwards
fine_tune_at = 700

# Freeze all the layers before the `fine_tune_at` layer
for layer in base_model.layers[:fine_tune_at]:
  layer.trainable =  False


# In[55]:


fine_tuning_learning_rate = 1e-5
model.compile(optimizer=tf.keras.optimizers.Adam(lr=fine_tuning_learning_rate),
              loss=tf.keras.losses.CategoricalCrossentropy(from_logits=True),
              metrics=METRICS)


# In[56]:


model.summary()


# In[57]:


len(model.trainable_variables)


# In[58]:


fine_tune_epochs = 15
total_epochs =  initial_epochs + fine_tune_epochs

history_fine = model.fit(train_dataset,
                         epochs=total_epochs,
                         initial_epoch=history.epoch[-1],
                         validation_data=validation_dataset)


# In[62]:


acc = []          
val_acc = []
loss = []     
val_loss = []

acc += history_fine.history['accuracy']
val_acc += history_fine.history['val_accuracy']

loss += history_fine.history['loss']
val_loss += history_fine.history['val_loss']



plt.figure(figsize=(8, 8))
plt.subplot(2, 1, 1)
plt.plot(acc, label='Training Accuracy')
plt.plot(val_acc, label='Validation Accuracy')
plt.ylim([0.8, 1])
plt.plot([initial_epochs-1,initial_epochs-1],
          plt.ylim(), label='Start Fine Tuning')
plt.legend(loc='lower right')
plt.title('Training and Validation Accuracy')
plt.ylabel('Accuracy')

plt.subplot(2, 1, 2)
plt.plot(loss, label='Training Loss')
plt.plot(val_loss, label='Validation Loss')
plt.ylim([0, 1.0])
plt.plot([initial_epochs-1,initial_epochs-1],
         plt.ylim(), label='Start Fine Tuning')
plt.legend(loc='upper right')
plt.title('Training and Validation Loss')
plt.xlabel('epoch')
plt.ylabel('Cross Entropy')
plt.show()


# In[63]:


plot_metrics(history_fine)


# In[64]:


result = model.evaluate(test_dataset)


# In[65]:


metrics = ["loss", "tp", "fp", "tn", "fn", "accuracy", "precision", "recall", "auc"]
for i in range(len(result)):
    print("{} : {}".format(metrics[i],round(result[i], 3)))


# In[66]:


#Retrieve a batch of images from the test set
image_batch, label_batch = test_dataset.as_numpy_iterator().next()
predictions = model.predict_on_batch(image_batch)

predictions = tf.nn.softmax(predictions)
predictions = list(np.argmax(x) for x in predictions.numpy())

print('Predictions:\n', predictions)
print('Labels:\n', list(np.argmax(x) for x in label_batch))

plt.figure(figsize=(10, 10))
for i in range(9):
  ax = plt.subplot(3, 3, i + 1)
  plt.imshow(image_batch[i].astype("uint8"))
  plt.title(class_names[predictions[i]])
  plt.axis("off")


# # Save Model

# In[67]:


# save model in JSON format
model_json = model.to_json()
json_file = open("model_weights.json", "w")
json_file.write(model_json)
print("Model saved in JSON format!")
    
# save training weights in h5 file
model.save_weights("model_weights.h5")
print("\nModel weights saved!")


# In[68]:


model.save("inception_V3.0_fineTuning.h5")


#!/usr/bin/env python
# coding: utf-8

# # Import Libraries

# In[23]:


#Importing and Installing Required Modules and Libraries
get_ipython().system('pip install tensorflow')
get_ipython().system('pip install basic_image_eda')
import os
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.colors as mcolors
import seaborn as sns
sns.set_theme(style="whitegrid")
import cv2
import sys
from re import sub
import tensorflow as tf
from tensorflow.keras import metrics
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import BatchNormalization
from tensorflow.keras.layers import Conv2D
from tensorflow.keras.layers import MaxPooling2D
from tensorflow.keras.layers import Activation, Flatten, Dropout, Dense
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.preprocessing import image
from tensorflow.keras import models
from tensorflow.keras.preprocessing import image_dataset_from_directory


# # Load Dataset

# In[24]:


data_dir = r"Downloads/plantvillage-dataset/color/"


# # Sample Image

# In[25]:


img = plt.imread(data_dir+"Apple___Apple_scab/00075aa8-d81a-4184-8541-b692b78d398a___FREC_Scab 3335.JPG")
plt.imshow(img)


# # Exploratory Data Analysis

# In[26]:


category_count = []

for root, dirs, files in os.walk(data_dir):
    for dir_path in dirs:
        category_count.append((dir_path, len(os.listdir(root+os.sep+dir_path))))
count_df = pd.DataFrame(category_count, columns=['Category', 'Count'])
count_df.head(10)


# In[27]:


total_images = count_df['Count'].sum()
total_images


# # Data Visualization by Category

# In[28]:


count_df = count_df.sort_values(by='Count', ascending=False)
plt.figure(figsize=(20,20))
sns.barplot(x="Count", y="Category", data=count_df)
plt.plot()


# In[29]:


healthy_images_count = count_df[count_df['Category'].str.endswith("healthy")]['Count'].sum()
healthy_images_count


# In[30]:


disease_images_count = total_images - healthy_images_count
disease_images_count


# In[31]:


temp_df = pd.DataFrame(data=[("Unhealthy", disease_images_count), ("Healthy", healthy_images_count)], columns=['Category', 'Count'])
sns.barplot(y="Count",x="Category", data=temp_df)
plt.plot()


# # Data Augmentation and Pre-processing

# In[32]:


BATCH_SIZE = 32
IMG_SIZE = (240, 240)


# In[33]:


train_dataset = image_dataset_from_directory(data_dir,
                                             shuffle=True,
                                             label_mode = 'categorical',
                                             validation_split = 0.2,
                                             batch_size=BATCH_SIZE,
                                             seed = 42,
                                             subset = "training",
                                             image_size=IMG_SIZE)

validation_dataset = image_dataset_from_directory(data_dir,
                                             shuffle=True,
                                             label_mode = 'categorical',
                                             validation_split = 0.2,
                                             batch_size=BATCH_SIZE,
                                             seed = 42,
                                             subset = "validation",
                                             image_size=IMG_SIZE)


# In[34]:


class_names = train_dataset.class_names
num_classes = len(class_names)
for i in range(1, num_classes + 1):
    print(str(i) + ". ", class_names[i - 1])


# In[35]:


val_batches = tf.data.experimental.cardinality(validation_dataset)
test_dataset = validation_dataset.take(val_batches // 5)
validation_dataset = validation_dataset.skip(val_batches // 5)

print('Number of validation batches: %d' % tf.data.experimental.cardinality(validation_dataset))
print('Number of test batches: %d' % tf.data.experimental.cardinality(test_dataset))


# In[36]:


AUTOTUNE = tf.data.AUTOTUNE

train_dataset = train_dataset.prefetch(buffer_size=AUTOTUNE)
validation_dataset = validation_dataset.prefetch(buffer_size=AUTOTUNE)
test_dataset = test_dataset.prefetch(buffer_size=AUTOTUNE)


# In[37]:


# added augmentations
data_augmentation = tf.keras.Sequential([
  tf.keras.layers.experimental.preprocessing.RandomFlip('horizontal'),
  tf.keras.layers.experimental.preprocessing.RandomRotation(0.2),
])


# In[38]:


for image, _ in train_dataset.take(1):
  plt.figure(figsize=(10, 10))
  first_image = image[0]
  for i in range(9):
    ax = plt.subplot(3, 3, i + 1)
    augmented_image = data_augmentation(tf.expand_dims(first_image, 0))
    plt.imshow(augmented_image[0] / 255)
    plt.axis('off')


# ## Metrics and Plotting functions

# In[39]:


def plot_metrics(history):
  colors = ['b', 'g', 'r', 'c', 'm', 'y', 'k', 'w']
  metrics = ['loss', 'auc', 'precision', 'recall']
  plt.figure(figsize=(20,10))
  for n, metric in enumerate(metrics):
    name = metric.replace("_"," ").capitalize()
    plt.subplot(2,2,n+1)
    plt.plot(history.epoch, history.history[metric], color=colors[0], label='Train')
    plt.plot(history.epoch, history.history['val_'+metric],
             color=colors[0], linestyle="--", label='Val')
    plt.xlabel('Epoch')
    plt.ylabel(name)
    if metric == 'loss':
      plt.ylim([0, plt.ylim()[1]])
    elif metric == 'auc':
      plt.ylim([0.8,1])
    else:
      plt.ylim([0,1])

    plt.legend()


# In[40]:


METRICS = [
      metrics.TruePositives(name='tp'),
      metrics.FalsePositives(name='fp'),
      metrics.TrueNegatives(name='tn'),
      metrics.FalseNegatives(name='fn'), 
      metrics.CategoricalAccuracy(name='accuracy'),
      metrics.Precision(name='precision'),
      metrics.Recall(name='recall'),
      metrics.AUC(name='auc')
]


# ## Load and compile model

# In[41]:


IMG_SHAPE = IMG_SIZE + (3,)


# In[42]:


preprocess_input = tf.keras.applications.inception_resnet_v2.preprocess_input


# In[43]:


base_model = tf.keras.applications.InceptionResNetV2(
                                include_top=False,
                                weights="imagenet",
                                input_shape=IMG_SHAPE,
                            )


# In[44]:


image_batch, label_batch = next(iter(train_dataset))
feature_batch = base_model(image_batch)
print(feature_batch.shape)


# In[45]:


base_model.trainable = False


# In[46]:


global_average_layer = tf.keras.layers.GlobalAveragePooling2D()
feature_batch_average = global_average_layer(feature_batch)

print(feature_batch_average.shape)


# In[47]:


prediction_layer = tf.keras.layers.Dense(num_classes, activation="softmax")
prediction_batch = prediction_layer(feature_batch_average)
print(prediction_batch.shape)


# In[48]:


inputs = tf.keras.Input(shape=(240, 240, 3))
x = data_augmentation(inputs)
x = preprocess_input(x)
x = base_model(x, training=False)
x = global_average_layer(x)
x = tf.keras.layers.Dropout(0.2)(x)
outputs = prediction_layer(x)
model = tf.keras.Model(inputs, outputs)


# In[49]:


base_learning_rate = 0.001
model.compile(optimizer=tf.keras.optimizers.Adam(lr=base_learning_rate),
              loss=tf.keras.losses.CategoricalCrossentropy(from_logits=True),
              metrics=METRICS)


# In[50]:


model.summary()


# # Training, Validation and Testing

# Before tuning

# In[51]:


initial_epochs = 10


# In[52]:


history = model.fit(train_dataset,
                    epochs=initial_epochs,
                    validation_data=validation_dataset)


# After tunung

# In[53]:


base_model.trainable = True


# In[54]:


# Let's take a look to see how many layers are in the base model
print("Number of layers in the base model: ", len(base_model.layers))

# Fine-tune from this layer onwards
fine_tune_at = 700

# Freeze all the layers before the `fine_tune_at` layer
for layer in base_model.layers[:fine_tune_at]:
  layer.trainable =  False


# In[55]:


fine_tuning_learning_rate = 1e-5
model.compile(optimizer=tf.keras.optimizers.Adam(lr=fine_tuning_learning_rate),
              loss=tf.keras.losses.CategoricalCrossentropy(from_logits=True),
              metrics=METRICS)


# In[56]:


model.summary()


# In[57]:


len(model.trainable_variables)


# In[58]:


fine_tune_epochs = 15
total_epochs =  initial_epochs + fine_tune_epochs

history_fine = model.fit(train_dataset,
                         epochs=total_epochs,
                         initial_epoch=history.epoch[-1],
                         validation_data=validation_dataset)


# In[62]:


acc = []          
val_acc = []
loss = []     
val_loss = []

acc += history_fine.history['accuracy']
val_acc += history_fine.history['val_accuracy']

loss += history_fine.history['loss']
val_loss += history_fine.history['val_loss']



plt.figure(figsize=(8, 8))
plt.subplot(2, 1, 1)
plt.plot(acc, label='Training Accuracy')
plt.plot(val_acc, label='Validation Accuracy')
plt.ylim([0.8, 1])
plt.plot([initial_epochs-1,initial_epochs-1],
          plt.ylim(), label='Start Fine Tuning')
plt.legend(loc='lower right')
plt.title('Training and Validation Accuracy')
plt.ylabel('Accuracy')

plt.subplot(2, 1, 2)
plt.plot(loss, label='Training Loss')
plt.plot(val_loss, label='Validation Loss')
plt.ylim([0, 1.0])
plt.plot([initial_epochs-1,initial_epochs-1],
         plt.ylim(), label='Start Fine Tuning')
plt.legend(loc='upper right')
plt.title('Training and Validation Loss')
plt.xlabel('epoch')
plt.ylabel('Cross Entropy')
plt.show()


# In[63]:


plot_metrics(history_fine)


# In[64]:


result = model.evaluate(test_dataset)


# In[65]:


metrics = ["loss", "tp", "fp", "tn", "fn", "accuracy", "precision", "recall", "auc"]
for i in range(len(result)):
    print("{} : {}".format(metrics[i],round(result[i], 3)))


# In[66]:


#Retrieve a batch of images from the test set
image_batch, label_batch = test_dataset.as_numpy_iterator().next()
predictions = model.predict_on_batch(image_batch)

predictions = tf.nn.softmax(predictions)
predictions = list(np.argmax(x) for x in predictions.numpy())

print('Predictions:\n', predictions)
print('Labels:\n', list(np.argmax(x) for x in label_batch))

plt.figure(figsize=(10, 10))
for i in range(9):
  ax = plt.subplot(3, 3, i + 1)
  plt.imshow(image_batch[i].astype("uint8"))
  plt.title(class_names[predictions[i]])
  plt.axis("off")


# # Save Model

# In[67]:


# save model in JSON format
model_json = model.to_json()
json_file = open("model_weights.json", "w")
json_file.write(model_json)
print("Model saved in JSON format!")
    
# save training weights in h5 file
model.save_weights("model_weights.h5")
print("\nModel weights saved!")


# In[68]:


model.save("inception_V3.0_fineTuning.h5")
#!/usr/bin/env python # coding: utf-8 # # Import Libraries # In[23]: #Importing and Installing Required Modules and Libraries # # Load Dataset # In[24]: # # Sample Image # In[25]: # # Exploratory Data Analysis # In[26]: # In[27]: # # Data Visualization by Category # In[28]: # In[29]: # In[30]: # In[31]: # # Data Augmentation and Pre-processing # In[32]: # In[33]: # In[34]: # In[35]: # In[36]: # In[37]: # added augmentations # In[38]: # ## Metrics and Plotting functions # In[39]: # In[40]: # ## Load and compile model # In[41]: # In[42]: # In[43]: # In[44]: # In[45]: # In[46]: # In[47]: # In[48]: # In[49]: # In[50]: # # Training, Validation and Testing # Before tuning # In[51]: # In[52]: # After tunung # In[53]: # In[54]: # Let's take a look to see how many layers are in the base model # Fine-tune from this layer onwards # Freeze all the layers before the `fine_tune_at` layer # In[55]: # In[56]: # In[57]: # In[58]: # In[62]: # In[63]: # In[64]: # In[65]: # In[66]: #Retrieve a batch of images from the test set # # Save Model # In[67]: # save model in JSON format # save training weights in h5 file # In[68]: