h5py: OSError: Unable to open file (unable to open file: name = 'C:\Users\(name blocked)\Scripts\Neural_Network.h5', errno = 2, error message = 'No such file or directory', flags = 0, o_flags = 0)

• Windows 10
• Python version 3.7
• Miniconda
• h5py version: latest I was trying to load a model with Keras and train it again.

Here is my code:

import gym
import random
import numpy as np
import tflearn
import os
import h5py
import tensorflow as tf
from tensorflow import keras
from keras.models import load_model
from tflearn.layers.core import input_data, dropout, fully_connected
from tflearn.layers.estimator import regression
from statistics import mean, median
from collections import Counter

LR = 1e-3

env = gym.make('CartPole-v0')
env.reset()

goal_steps = 300

score_requirement = 50

initial_games = 10000

def some_random_games_first():
    for episode in range (5):
        env.reset()
        for t in range(goal_steps):
            env.render()
            action = env.action_space.sample()
            observation, reward, done, info = env.step(action)
            if done:
                break

def intial_population():
	training_data = []
	scores = []
	accepted_scores = [] 
	for _ in range (initial_games):
		score = 0
		game_memory = []
		prev_observation = []
		for _ in range (goal_steps):
			action = random.randrange (0,2)
			observation, reward, done, info = env.step(action)

			if len(prev_observation) > 0:
				game_memory.append([prev_observation, action])

			prev_observation = observation
			score += reward
			if done:
				break
		if score >= score_requirement:
			accepted_scores.append(score)
			for data in game_memory:
				if data [1] == 1:
					output = [0, 1]
				elif data [1] == 0:
					output = [1, 0]

				training_data.append([data[0], output])
		env.reset()
		scores.append(score)
	training_data_save = np.array (training_data)
	np.save ("saved.npy", training_data_save)
	print ("Average accepted scores", mean(accepted_scores))
	print("Median accepted score: ", median(accepted_scores))
	print (Counter (accepted_scores))

	return training_data
intial_population()

def neural_network_model (input_size):
	network = input_data (shape=[None, input_size, 1], name="input")
	network = fully_connected(network, 128, activation="relu")
	network = dropout(network, 0.8)

	network = fully_connected(network, 256, activation="relu")
	network = dropout(network, 0.8)

	network = fully_connected(network, 512, activation="relu")
	network = dropout(network, 0.8)

	network = fully_connected(network, 256, activation="relu")
	network = dropout(network, 0.8)

	network = fully_connected(network, 128, activation="relu")
	network = dropout(network, 0.8)

	network = fully_connected(network, 2, activation="softmax")
	network = regression(network, optimizer="adam", learning_rate = LR, loss = "categorical_crossentropy", name = 'targets')
	model = tflearn.DNN (network, tensorboard_dir='log")

	return model

def train_model(training_data, model=False):
	X = np.array ([i [0] for i in training_data]).reshape(-1, len(training_data[0][0]), 1)
	y = [i [1] for i in training_data]

	if not model:
		model = neural_network_model (input_size = len(X[0]))
	model.fit({'input' :X}, {'targets' :y}, n_epoch=3, snapshot_step=500, show_metric=True,
		run_id='openaistuff')
	return model

training_data = intial_population()
model = train_model(training_data)


scores = [] 
choices = []

for each_game in range(1):
	score = 0
	game_memory= []
	prev_obs = []
	env.reset()
	for _ in range (goal_steps):
		env.render()
		if len(prev_obs) == 0:
			action = random.randrange (0,2)
		else:
			action = np.argmax(model.predict(prev_obs.reshape(-1, len(prev_obs),1)) [0])
		choices.append(action)

		new_observation, reward, done, info = env.step(action)
		prev_obs = new_observation
		game_memory.append ([new_observation, action])
		score += reward
		if done:
			break
	scores.append(score)
	model.save("C:\\Users\\William\\Scripts\\Neural_Network.h5")
Average_Score = sum(scores)/len(scores)
print("Average Score: ", Average_Score)
print("choice 1:{}  choice 0:{}".format(choices.count(1)/len(choices),choices.count(0)/len(choices)))
print(score_requirement)
if Average_Score > 299:
	print("Solved")
else:
	print("Not solved, trying again")
	new_model = load_model("C:\\Users\\William\\Scripts\\Neural_Network.h5")'

And when I would run the program I would get this error:

Traceback (most recent call last):
  File "C:\Users\William\Scripts\Cartpole.py", line 157, in <module>
    new_model = load_model('C:\\Users\\William\\Scripts\\Neural_Network.h5')
  File "C:\Users\William\Miniconda3\envs\tensorflow\lib\site-packages\keras\engine\saving.py", line 417, in load_model
    f = h5dict(filepath, 'r')
  File "C:\Users\William\Miniconda3\envs\tensorflow\lib\site-packages\keras\utils\io_utils.py", line 186, in __init__
    self.data = h5py.File(path, mode=mode)
  File "C:\Users\William\Miniconda3\envs\tensorflow\lib\site-packages\h5py\_hl\files.py", line 394, in __init__
    swmr=swmr)
  File "C:\Users\William\Miniconda3\envs\tensorflow\lib\site-packages\h5py\_hl\files.py", line 170, in make_fid
    fid = h5f.open(name, flags, fapl=fapl)
  File "h5py\_objects.pyx", line 54, in h5py._objects.with_phil.wrapper
  File "h5py\_objects.pyx", line 55, in h5py._objects.with_phil.wrapper
  File "h5py\h5f.pyx", line 85, in h5py.h5f.open
OSError: Unable to open file (unable to open file: name = 'C:\Users\William\Scripts\Neural_Network.h5', errno = 2, error message = 'No such file or directory', flags = 0, o_flags = 0)

I have looked at the file and it generates the files: checkpoint,(thing used for the file type ‘file’), Neural_Network.h5.data-00000-of-00001, Neural_Network.h5, and Neural_Network.h5.meta. I see the ‘Neural_Network.h5’ so I am not sure why it cannot load it. Any answers are appreciated!

Edit: sorry for bad code formatting I saw it and cannot seem to fix it