FlyAI竞赛使用tensorflow2.0进行海底物种分类

这是FlyAI的一个数据竞赛题目，使用tensorflow2.0构建卷积神经网络对海底的鱼类物种进行分类，数据集可以在FlyAI官网进行下载。

import tensorflow as tf
import pandas as pd
import cv2
import numpy as npdef proprecess(x,y):x = tf.cast(x,dtype=tf.float32) / 255.0y = tf.cast(y,dtype=tf.int32)return x,ydf = pd.read_csv(r'F:\fish\FishClassification_FlyAI\data\input\FishClassification\train.csv')
img_path_list = df['image_path'].values
img_label_list = df['label'].valuespath = 'F:\\fish\\FishClassification_FlyAI\\data\\input\\FishClassification\\'
print(len(img_path_list))
print(len(img_label_list))
train_x = []
train_y = []
test_x = []
test_y = []for i in range(len(img_path_list)):img_path = path + img_path_list[i]imgs = cv2.imread(img_path,-1)imgs = cv2.resize(imgs,(128,128))if i % 10 == 0:test_x.append(imgs)test_y.append(img_label_list[i])else:train_x.append(imgs)train_y.append(img_label_list[i])print(len(train_x) + len(test_x))
print(len(train_y) + len(test_y))train_x = np.array(train_x,dtype=tf.float32)
train_y = np.array(train_y,dtype=tf.int32)
test_x = np.array(test_x,dtype=tf.float32)
test_y = np.array(test_y,dtype=tf.int32)train_db = tf.data.Dataset.from_tensor_slices((train_x,train_y))
train_db = train_db.map(proprecess).shuffle(1000).batch(32)
test_db = tf.data.Dataset.from_tensor_slices((test_x,test_y))
test_db = test_db.map(proprecess).batch(32)class Mymodel(tf.keras.Model):def __init__(self):super().__init__()self.conv1 = tf.keras.layers.Conv2D(filters=32,kernel_size=[3,3],padding='same',activation=tf.nn.relu)self.pool1 = tf.keras.layers.MaxPool2D(pool_size=[2,2],strides=[2,2])self.conv2 = tf.keras.layers.Conv2D(filters=64,kernel_size=[3,3],padding='same',activation=tf.nn.relu)self.pool2 = tf.keras.layers.MaxPool2D(pool_size=[2,2],strides=[2,2])self.flatten = tf.keras.layers.Flatten()self.fc1 = tf.keras.layers.Dense(64,activation=tf.nn.relu)self.fc2 = tf.keras.layers.Dense(21,activation=tf.nn.softmax)def call(self,inputs):x = self.conv1(inputs)x = self.pool1(x)x = self.conv2(x)x = self.pool2(x)x = self.flatten(x)x = self.fc1(x)x = self.fc2(x)return xmodel = Mymodel()
model.build(input_shape=(None,128,128,3))
model.summary()
optimizer = tf.keras.optimizers.Adam(learning_rate=0.001)for epoch in range(10):train_loss = 0train_num = 0for x,y in train_db:x = tf.reshape(x,[-1,128,128,3])with tf.GradientTape() as tape:pred = model(x)pred = tf.cast(pred,dtype=tf.int32)loss = tf.keras.losses.sparse_categorical_crossentropy(y_true=y,y_pred=pred)loss = tf.reduce_mean(loss)grads = tape.gradient(loss,model.trainable_variables)optimizer.apply_gradients(zip(grads,model.trainable_variables))train_loss += losstrain_num += x.shape[0]loss = float(train_loss / train_num)total_correct = 0total_num = 0for x,y in test_db:x = tf.reshape(x,[-1,128,128,3])pred = model(x)pred = tf.argmax(pred,axis=1)pred = tf.cast(pred,dtype=tf.int32)correct = tf.equal(pred,y)correct = tf.reduce_sum(tf.cast(correct,dtype=tf.int32))total_correct += correcttotal_num += x.shape[0]accuracy = float(total_correct / total_num)print(epoch,'train_loss:',train_loss,'accuracy:',accuracy)print('............................prediction..........................')
for x,y in test_db:img = xlabel = ybreak
img = tf.reshape(img,[-1,128,128,3])
pred = model(img)
pred = tf.argmax(pred,axis=1)
pred = tf.cast(pred,dtype=tf.int32)
print('pred:',pred)
print('label:',label)
print('The pred is equal the label',tf.equal(pred,label))

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