Feat: generated network for classification problem of cat/dog

Feat: first version of dataGatherer client

predictor/Predictor.py

@@ -1,4 +1,3 @@
-
 from keras.models import Sequential
 from keras.layers import Activation, Dropout, Flatten, Dense
 from keras import backend as K
@@ -10,23 +9,12 @@ import matplotlib.image as mpimg
 import cv2
 import numpy as np
 
-import tensorflow as tf
-sess = tf.Session(config=tf.ConfigProto(log_device_placement=True))
-
-
-
-#from keras.datasets import imdb
-
-
-
-#https://gist.github.com/fchollet/0830affa1f7f19fd47b06d4cf89ed44d
-
 img_width, img_height = 150, 150
-train_data_dir = './datas/train'
-validation_data_dir = './datas/test'
+train_data_dir = './UserflowPredictorSystem/predictor/datas/train'
+validation_data_dir = './UserflowPredictorSystem/predictor/datas/test'
 nb_train_samples = 2000
 nb_validation_samples = 800
-epochs = 10
+epochs = 100
 batch_size = 16
 
 
@@ -35,114 +23,99 @@ if K.image_data_format() == 'channels_first':
 else:
     input_shape = (img_width, img_height, 3)
 
+def create_model():
+    model = Sequential()
+    model.add(Conv2D(32, (3, 3), input_shape=input_shape))
+    model.add(Activation('relu'))
+    model.add(MaxPooling2D(pool_size=(2, 2)))
 
-model = Sequential()
-model.add(Conv2D(32, (3, 3), input_shape=input_shape))
-model.add(Activation('relu'))
-model.add(MaxPooling2D(pool_size=(2, 2)))
+    model.add(Conv2D(32, (3, 3)))
+    model.add(Activation('relu'))
+    model.add(MaxPooling2D(pool_size=(2, 2)))
 
-model.add(Conv2D(32, (3, 3)))
-model.add(Activation('relu'))
-model.add(MaxPooling2D(pool_size=(2, 2)))
+    model.add(Conv2D(64, (3, 3)))
+    model.add(Activation('relu'))
+    model.add(MaxPooling2D(pool_size=(2, 2)))
 
-model.add(Conv2D(64, (3, 3)))
-model.add(Activation('relu'))
-model.add(MaxPooling2D(pool_size=(2, 2)))
-
-model.add(Flatten())
-model.add(Dense(64))
-model.add(Activation('relu'))
-model.add(Dropout(0.5))
-model.add(Dense(1))
-model.add(Activation('sigmoid'))
+    model.add(Flatten())
+    model.add(Dense(64))
+    model.add(Activation('relu'))
+    model.add(Dropout(0.5))
+    model.add(Dense(1))
+    model.add(Activation('sigmoid'))
 
 
-model.compile(loss='binary_crossentropy',
-              optimizer='rmsprop',
-              metrics=['accuracy'])
+    model.compile(loss='binary_crossentropy',
+                  optimizer='rmsprop',
+                  metrics=['accuracy'])
+    return model
 
-# this is the augmentation configuration we will use for training
-train_datagen = ImageDataGenerator(
-    rescale=1. / 255,
-    shear_range=0.2,
-    zoom_range=0.2,
-    horizontal_flip=True)
+def train_model(model):
+    # this is the augmentation configuration we will use for training
+    train_datagen = ImageDataGenerator(
+        rescale=1. / 255,
+        shear_range=0.2,
+        zoom_range=0.2,
+        horizontal_flip=True)
 
-# this is the augmentation configuration we will use for testing:
-# only rescaling
-test_datagen = ImageDataGenerator(rescale=1. / 255)
+    # this is the augmentation configuration we will use for testing:
+    # only rescaling
+    test_datagen = ImageDataGenerator(rescale=1. / 255)
 
-train_generator = train_datagen.flow_from_directory(
-    train_data_dir,
-    target_size=(img_width, img_height),
-    batch_size=batch_size,
-    class_mode='binary')
+    train_generator = train_datagen.flow_from_directory(
+        train_data_dir,
+        target_size=(img_width, img_height),
+        batch_size=batch_size,
+        class_mode='binary')
 
-validation_generator = test_datagen.flow_from_directory(
-    validation_data_dir,
-    target_size=(img_width, img_height),
-    batch_size=batch_size,
-    class_mode='binary')
+    validation_generator = test_datagen.flow_from_directory(
+        validation_data_dir,
+        target_size=(img_width, img_height),
+        batch_size=batch_size,
+        class_mode='binary')
 
-model.fit_generator(
-    train_generator,
-    steps_per_epoch=nb_train_samples // batch_size,
-    epochs=epochs,
-    validation_data=validation_generator,
-    validation_steps=nb_validation_samples // batch_size)
+    model.fit_generator(
+        train_generator,
+        steps_per_epoch=nb_train_samples // batch_size,
+        epochs=epochs,
+        validation_data=validation_generator,
+        validation_steps=nb_validation_samples // batch_size)
 
-from tensorflow.python.client import device_lib
-print(device_lib.list_local_devices())
-
-from keras import backend as K
-K.tensorflow_backend._get_available_gpus()
-
-model.save_weights('first_try.h5')
-model.summary()
-
-img = cv2.imread("./datas/test/27.jpg")
-im = mpimg.imread("./datas/test/27.jpg")
-plt.imshow(im)
-
-img = cv2.resize(img, (img_width,img_height))
-print(img.shape)
-img = img.reshape(1, img_width, img_height, 3)
-
-print(img.shape)
-#print(np.argmax(loaded_model.predict(img)))
-print(model.predict(img))
+    model.save_weights('./UserflowPredictorSystem/first_try2.h5')
+    return model
 
 
 
+def load_trained_model(weights_path):
+   model = create_model()
+   model.load_weights(weights_path)
+   return model
+
+
+def predict(number, model):
+    img = cv2.imread("./UserflowPredictorSystem/predictor/datas/test/" + str(number) + ".jpg")
+    im = mpimg.imread("./UserflowPredictorSystem/predictor/datas/test/" + str(number) + ".jpg")
+    plt.imshow(im)
+    img = cv2.resize(img, (img_width,img_height))
+    img = img.reshape(1, img_width, img_height, 3)
+    res = model.predict(img)
+    if res == 1:
+        print('DOG')
+    else:
+        print('CAT')
+
+model = create_model()
+model = train_model(model)
+
+import os
+os.getcwd()
+
+trained_model = load_trained_model("./UserflowPredictorSystem/first_try2.h5")
+trained_model.summary()
+import random
+predict(random.randint(1,12500), trained_model)
+predict('lolly', model)
 
 
 
-
-
-K.tensorflow_backend._get_available_gpus()
-
-
-num_words = 10000
-(X_train, y_train), (X_test, y_test) = loadDatas()
-
-maxlen = 500
-
-X_train = pad_sequences(X_train, maxlen=maxlen)
-X_test = pad_sequences(X_test, maxlen=maxlen)
-
-from keras.layers import Flatten, LSTM
-from keras.layers.convolutional import Conv1D, MaxPooling1D
-
-model = Sequential()
-model.add(Embedding(num_words, 50, input_length=500))
-model.add(Conv1D(filters=32, kernel_size=3, padding="same", activation="relu"))
-model.add(MaxPooling1D(pool_size=2))
-model.add(LSTM(32, dropout=0.4))
-model.add(Dense(1, activation="sigmoid"))
-
-model.summary()
-
-model.compile(loss="binary_crossentropy", optimizer="rmsprop", metrics=["accuracy"])
-model.fit(X_train, y_train, batch_size=512, validation_split=0.2, epochs=5)
-
-model.evaluate(X_test, y_test)
+print(np.argmax(loaded_model.predict(img)))

predictor/Readme.md

@@ -0,0 +1 @@
+.

predictor/Untitled.ipynb

@@ -1,53 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "<tensorflow.python.client.session.Session object at 0x7fe921736b38>\n"
-     ]
-    }
-   ],
-   "source": [
-    "\n",
-    "import tensorflow as tf\n",
-    "\n",
-    "sess = tf.Session(config=tf.ConfigProto(log_device_placement=True))\n",
-    "print(sess)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.2"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}