ajinkyajawale14499

// code goes here!
#include <iostream>
#include <queue>
using namespace std;

// node structure

struct Node
{
    int data;

ajinkyajawale14499

import java.util.*;
class Node 
{ 
    int data; 
    Node left, right; 
    public Node(int item) 
    { 
        data = item; 
        left = right = null; 
    } 

ajinkyajawale14499

//code goes here!
#include <bits/stdc++.h>
#include <iostream> 
#include <algorithm>
using namespace std;

class Node
{
    public: 
    int data;

ajinkyajawale14499

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')

ajinkyajawale14499

saved_model_dir = 'save/fine_tuning'
tf.saved_model.save(model, saved_model_dir)

converter = tf.lite.TFLiteConverter.from_saved_model(saved_model_dir)
tflite_model = converter.convert()

with open('model.tflite', 'wb') as f:
  f.write(tflite_model)
  
# download the model & labels  

ajinkyajawale14499

model.compile(loss='categorical_crossentropy',
              optimizer = tf.keras.optimizers.Adam(1e-5),
              metrics=['accuracy'])

ajinkyajawale14499

# 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 = 100

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

ajinkyajawale14499

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

loss = history.history['loss']
val_loss = history.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')

ajinkyajawale14499

epochs = 10

history = model.fit(train_generator, 
                    epochs=epochs, 
                    validation_data=val_generator)

ajinkyajawale14499

model.compile(optimizer=tf.keras.optimizers.Adam(), 
              loss='categorical_crossentropy', 
              metrics=['accuracy'])