save_best_only=True), tensorboard,
EarlyStopping(monitor='val_loss', patience=5, verbose=1)
]
print('=> created callback objects <=')
print('=> initializing training loop <=')
history = model.fit_generator(train_generator,
steps_per_epoch=train_steps,
epochs=epochs,
validation_data=validation_generator,
validation_steps=val_steps,
workers=8,
use_multiprocessing=True,
max_queue_size=500,
callbacks=callbacks_list)
print('=> loading best weights <=')
model.load_weights(final_weights_path)
print('=> saving final model <=')
pmodel.save(
os.path.join(os.path.abspath(model_path),
'Weights/model_InceptionResnetV2_15Layer.h5'))
#Load-Model #Not Required #Comment Out All Lines When Required
#new_model=tf.keras.models.load_model('/home/jediyoda/Maharshi/Coffee-Table-Material/Weights/model_InceptionResnetV2_1Layer.h5')
#new_model.summary()
#Predictions
predictions = []
img_path = Changes_To_Be_Made.Image_Path #Set This To The Val Directory Path
CSV_Name = Changes_To_Be_Made.Csv_Name #Set CSV Name To Be Generated
# Running the model
history = model.fit_generator(train_generator,
steps_per_epoch=train_steps,
epochs=epochs,
validation_data=validation_generator,
validation_steps=val_steps,
workers=2,
use_multiprocessing=False,
max_queue_size=500,
callbacks=callbacks_list)
filepath = os.path.join(os.path.abspath(model_path),
'Weights/top_model_weights_' + 'Frozen_Layers.h5')
print('=> loading best weights <=')
model.load_weights(filepath)
print('=> saving final model <=')
Final_Weights = 'Weights/model_' + architecture_name + "_" + str(
layers_frozen) + 'Frozen_Layers.h5'
pmodel.save(os.path.join(os.path.abspath(model_path), Final_Weights))
# These steps are only for checking predictions and only needed
# to run if you need to check predictions on your data
# Loading the model with best weights
new_model = tf.keras.models.load_model(filepath)
#new_model.summary()
#Predictions
import cv2
from keras.models import Sequential
from keras.models import load_weights
from keras.layers import Convolution2D, MaxPooling2D
from keras.layers import Activation, Dropout, Flatten, Dense
import h5py
import numpy as np
categories = ['chair', 'elephant', 'camera', 'flamingo', 'butterfly']
nb_classes = len(categories)
model = load_weights('./5obj-model.hdf5')
data_dir = './data/'
image_w = 64
image_h = 64
pixels = image_w * image_h * 3
img = cv2.imread('./data/butterfly/00000.jpg')
img = cv2.resize(img, (image_w, image_h))
predict = model.predict([img])
print(predict)
name = categories[pre[0].argmax()]
save_dir = './data/' + name
os.makedirs(save_dir, exist_ok=True)
import pandas as pd
import re
import os
import tensorflow as tf
from numpy import array
import keras
from keras.layers import Input, Lambda, Dense
from keras.models import Model
import keras.backend as K
from keras.preprocessing import sequence
from keras.models import load_model, load_weights
app = Flask(__name__)
MODEL_PATH = '/home/akhil/Downloads/deep_learning/elmo/elmo_model.h5'
model = load_weights(MODEL_PATH)
@app.route('/')
def home():
return render_template('index.html')
@app.route('/predict', methods=['POST'])
def predict():
if request.method == 'POST':
message = request.form['message']
data = [message]
vect = model.transform(data).toarray()
my_prediction = classifier.predict(vect)
return render_template('result.html', prediction=my_prediction)
if __name__ == '__main__':
#coding=utf8
import keras
from keras.models import load_weights
model = load_weights('/home/smallchild/keras-yolo3/model_data/trained_weights_final.h5')
model.summary()