First commit

.gitignore

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+datas

predictor/.ipynb_checkpoints/Untitled-checkpoint.ipynb

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+{
+ "cells": [],
+ "metadata": {},
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

predictor/Predictor.py

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+
+from keras.models import Sequential
+from keras.layers import Activation, Dropout, Flatten, Dense
+from keras import backend as K
+from keras.preprocessing.sequence import pad_sequences
+from keras.preprocessing.image import ImageDataGenerator
+from keras.layers import Conv2D, MaxPooling2D
+import matplotlib.pyplot as plt
+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'
+nb_train_samples = 2000
+nb_validation_samples = 800
+epochs = 10
+batch_size = 16
+
+
+if K.image_data_format() == 'channels_first':
+    input_shape = (3, img_width, img_height)
+else:
+    input_shape = (img_width, img_height, 3)
+
+
+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(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.compile(loss='binary_crossentropy',
+              optimizer='rmsprop',
+              metrics=['accuracy'])
+
+# 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)
+
+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')
+
+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))
+
+
+
+
+
+
+
+
+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)

predictor/Untitled.ipynb

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+{
+ "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
+}