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全连接神经网络FCNN
Tensorflow 2.0
import warnings
import seaborn as sns
import pandas as pd
import tensorflow as tf
import matplotlib.pyplot as plt
from tensorflow import keras
from tensorflow.keras import layers, optimizers, datasets
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score, classification_report, confusion_matrix
warnings.filterwarnings('ignore')
%matplotlib inline
# Min-Max Normalization
def min_max_normalization(x, y):
x = tf.cast(x, tf.float32)/255.0
y = tf.cast(y, tf.int64)
return x, y
# https://www.kaggle.com/c/digit-recognizer/data
def load_data(path):
data = pd.read_csv(path)
data_x = data.drop(['label'], axis=1).values
data_y = data.label.values
train_x, valid_x, train_y, valid_y = train_test_split(data_x, data_y, test_size=100)
# 切分传入Tensor的第一个维度，生成相应的dataset
train_ds = tf.data.Dataset.from_tensor_slices((train_x, train_y))
#valid_ds = tf.data.Dataset.from_tensor_slices((valid_x, valid_y))
valid_x = valid_x/255.0
valid_ds = (valid_x, valid_y)
train_ds = train_ds.map(min_max_normalization)
train_ds = train_ds.take(10000).shuffle(10000).batch(1000)
return train_ds, valid_ds
# softmax cross entropy
def loss_function(labels, pred_proba):
loss = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=labels, logits=pred_proba)
return tf.reduce_mean(loss)
def compute_accuracy(pred_proba, labels):
predictions = tf.argmax(pred_proba, axis=1)
return tf.reduce_mean(tf.cast(tf.equal(predictions, labels), tf.float32))
def train(epoch):
model = keras.Sequential([layers.Dense(128, activation='relu'),
layers.Dense(128, activation='relu'),
layers.Dense(10)])
optimizer = optimizers.Adam()
trainloss_list = [] # every step loss
trainaccuracy_list = []
validaccuracy_list = []
train_ds, valid_ds = load_data('../DataSet/mnist_csv/train.csv')
for i in range(epoch):
for step,(x, y) in enumerate(train_ds):
with tf.GradientTape() as gdt:
pred_proba = model(x)
predictions = tf.argmax(pred_proba, axis=1)
valid_preds = tf.argmax(model(valid_ds[0]), axis=1)
tloss_ = loss_function(y, pred_proba)
taccuracy_ = accuracy_score(y, predictions)
vaccuracy_ = accuracy_score(valid_ds[1], valid_preds)
trainloss_list.append(tloss_)
trainaccuracy_list.append(taccuracy_)
validaccuracy_list.append(vaccuracy_)
# compute gradient
grads = gdt.gradient(tloss_, model.trainable_variables)
# update weights
optimizer.apply_gradients(zip(grads, model.trainable_variables))
if step%1000==0:
print('Train loss is : %f, Train accuracy is : %f, Valid accuracy is : %f'%(tloss_, taccuracy_, vaccuracy_))
cfm = confusion_matrix(valid_ds[1], tf.argmax(model(valid_ds[0]), axis=1))
return trainloss_list, trainaccuracy_list, validaccuracy_list, cfm
def main():
tloss, taccu, vaccu, cfm = train(20)
plt.figure(figsize=(20, 11))
plt.subplot(2, 2, 1)
plt.title('Train loss & accuracy')
plt.plot(tloss, label='loss')
plt.plot(taccu, label='accuracy')
plt.grid(axis='y')
plt.legend()
plt.subplot(2, 2, 2)
plt.title('Train valid accuracy')
plt.plot(taccu, label='Train')
plt.plot(vaccu, label='Valid')
plt.legend()
plt.grid(axis='y')
plt.subplot(2, 2, 3)
plt.title('Confusion matrix')
sns.heatmap(cfm)
plt.subplot(2, 2, 4)
plt.title('Tensorflow 2.0')
plt.imshow(tf_pic)
plt.grid()
if __name__ == '__main__':
main()
Train loss is : 2.306837, Train accuracy is : 0.057000, Valid accuracy is : 0.080000
Train loss is : 1.265308, Train accuracy is : 0.763000, Valid accuracy is : 0.710000
Train loss is : 0.618489, Train accuracy is : 0.846000, Valid accuracy is : 0.770000
Train loss is : 0.425268, Train accuracy is : 0.870000, Valid accuracy is : 0.870000
Train loss is : 0.340226, Train accuracy is : 0.896000, Valid accuracy is : 0.870000
Train loss is : 0.288782, Train accuracy is : 0.915000, Valid accuracy is : 0.880000
Train loss is : 0.255068, Train accuracy is : 0.922000, Valid accuracy is : 0.920000
Train loss is : 0.234775, Train accuracy is : 0.930000, Valid accuracy is : 0.940000
Train loss is : 0.219541, Train accuracy is : 0.937000, Valid accuracy is : 0.940000
Train loss is : 0.195807, Train accuracy is : 0.941000, Valid accuracy is : 0.940000
Train loss is : 0.174473, Train accuracy is : 0.958000, Valid accuracy is : 0.940000
Train loss is : 0.160328, Train accuracy is : 0.963000, Valid accuracy is : 0.940000
Train loss is : 0.148957, Train accuracy is : 0.966000, Valid accuracy is : 0.940000
Train loss is : 0.138590, Train accuracy is : 0.965000, Valid accuracy is : 0.940000
Train loss is : 0.129336, Train accuracy is : 0.968000, Valid accuracy is : 0.950000
Train loss is : 0.120952, Train accuracy is : 0.968000, Valid accuracy is : 0.950000
Train loss is : 0.113196, Train accuracy is : 0.970000, Valid accuracy is : 0.950000
Train loss is : 0.106510, Train accuracy is : 0.971000, Valid accuracy is : 0.950000
Train loss is : 0.099868, Train accuracy is : 0.973000, Valid accuracy is : 0.950000
Train loss is : 0.093243, Train accuracy is : 0.975000, Valid accuracy is : 0.950000
