predict.py -error

gistfile1.txt

# this code does not work
# error 
# chainer.utils.type_check.InvalidType: 
# Invalid operation is performed in: Convolution2DFunction (Forward)
# 
# Expect: in_types[0].ndim == 4
# Actual: 2 != 4

from flask import Flask, render_template, request, redirect, url_for

import numpy as np
from PIL import Image

import chainer
from chainer import cuda, Function, gradient_check, report, training, utils, Variable
from chainer import datasets, iterators, optimizers, serializers
from chainer import Link, Chain, ChainList
import chainer.functions as F
import chainer.links as L
from chainer.datasets import tuple_dataset
from chainer import training
from chainer.training import extensions

from datetime import datetime

"""
# Network definition 
class MLP(chainer.Chain):
    def __init__(self, n_units, n_out):
        super(MLP, self).__init__()
        with self.init_scope():
            # the size of the inputs to each layer will be inferred
            self.l1 = L.Linear(None, n_units)  # n_in -> n_units
            self.l2 = L.Linear(None, n_units)  # n_units -> n_units
            self.l3 = L.Linear(None, n_out)  # n_units -> n_out
    def __call__(self, x):
        h1 = F.relu(self.l1(x))
        h2 = F.relu(self.l2(h1))
        return self.l3(h2)
"""

#2 モデルの記述
class MyModel(Chain):
    # パラメータを含む関数の宣言
    def __init__(self):
        super(MyModel, self).__init__(
            cn1=L.Convolution2D(1,20,5),
            cn2=L.Convolution2D(20,50,5),
            fc1=L.Linear(800,500),
            fc2=L.Linear(500,10),
        )
    
    # 損失関数
    def __call__(self, x,t):
        return F.softmax_cross_entropy(self.fwd(x),t)

    def fwd(self, x):
        h1 = F.max_pooling_2d(F.relu(self.cn1(x)),2)
        h2 = F.max_pooling_2d(F.relu(self.cn2(h1)),2)
        h3 = F.dropout(F.relu(self.fc1(h2)))
        return self.fc2(h3)
 
app = Flask(__name__)
@app.route('/', methods = ['GET', 'POST'])
def upload_file():
  if request.method == 'GET':
    return render_template('index.html')
  if request.method == 'POST':
    # アプロードされたファイルを保存する
    f = request.files['file']
    filepath = "./static/" + datetime.now().strftime("%Y%m%d%H%M%S") + ".png"
    f.save(filepath)
    # モデルを使って判定する
    # model = L.Classifier(MyModel(1000, 10))
    model = MyModel()
    optimizer = optimizers.Adam()
    optimizer.setup(model)
    # serializers.load_npz('lcd2.model', model)
    serializers.load_hdf5('mnist-cnn.model', model)
    image = Image.open(filepath).convert('L')
    image = np.asarray(image).astype(np.float32) / 255
    # image = image.reshape((1, -1))
    x = Variable(image)
    # y = model.predictor(x) #
    out = model.fwd(x)
    # predict = np.argmax(y.data)
    predict = np.argmax(out.data)
    return render_template('index.html', filepath = filepath , predict = predict )
if __name__ == '__main__':
  app.run(host="0.0.0.0", port=int("5000"),debug=True)