Notebooks
MetOcean Solutions Observation API connector
Description:
This notebook describes how to get observation data from one of MetOcean Solutions' observation API. The data that can be accessed though the API come from different sources such as MetService's automatic weather station network, a number of wave buoys and tidal gages owned by various organisations. Different instances of the API are used for different sources. This connector should work for all of them. The API provides the data as sets of records that belong to a station. Each station is identified by a unique id string. Each station has its own metadata describing location, instrument, ownership, status, license and level of permission. If the station is not open then anyone interested to use the data needs to get in contact with MetOcean to negotiate access to the data. Any access to data non data will be done using the same connector but authentication will need to be set when initialising the connector. For now the connector allows to retrieve data in the shape of xarray datasets. A complete documentation of the API can be found here: https://observations-open.metoceanapi.com/api/swagger/
Metadata:
Author:
Sebastien Delaux
Licenses:
Creative Commons Attribution 4.0 International, Creative Commons Attribution-ShareAlike 4.0 International, GNU GPLv3
Datasets:
Content:
Installation of required libraries
Install and import xarray and matplotlib in the current Jupyter kernel. Xarray is a great library to work with data stored using NetCDF format. MetOcean's Observation API can provide data under the form of netCDF-like JSON and hence xarray is a great option to work with that data. Matplotlib will used to plot the data.
In [1]:
import sys !{sys.executable} -m pip install xarray matplotlib import xarray
Collecting xarray Using cached https://files.pythonhosted.org/packages/ee/11/fb2a8a6015e3de4ff19a4870bb0d11f48ebdd997062557d24cd076b3088f/xarray-0.15.1-py3-none-any.whl Collecting matplotlib Using cached https://files.pythonhosted.org/packages/93/4b/52da6b1523d5139d04e02d9e26ceda6146b48f2a4e5d2abfdf1c7bac8c40/matplotlib-3.2.1-cp36-cp36m-manylinux1_x86_64.whl Collecting numpy>=1.15 (from xarray) Using cached https://files.pythonhosted.org/packages/07/08/a549ba8b061005bb629b76adc000f3caaaf881028b963c2e18f811c6edc1/numpy-1.18.2-cp36-cp36m-manylinux1_x86_64.whl Collecting setuptools>=41.2 (from xarray) Using cached https://files.pythonhosted.org/packages/a0/df/635cdb901ee4a8a42ec68e480c49f85f4c59e8816effbf57d9e6ee8b3588/setuptools-46.1.3-py3-none-any.whl Collecting pandas>=0.25 (from xarray) Using cached https://files.pythonhosted.org/packages/bb/71/8f53bdbcbc67c912b888b40def255767e475402e9df64050019149b1a943/pandas-1.0.3-cp36-cp36m-manylinux1_x86_64.whl Collecting python-dateutil>=2.1 (from matplotlib) Using cached https://files.pythonhosted.org/packages/d4/70/d60450c3dd48ef87586924207ae8907090de0b306af2bce5d134d78615cb/python_dateutil-2.8.1-py2.py3-none-any.whl Collecting kiwisolver>=1.0.1 (from matplotlib) Using cached https://files.pythonhosted.org/packages/f8/a1/5742b56282449b1c0968197f63eae486eca2c35dcd334bab75ad524e0de1/kiwisolver-1.1.0-cp36-cp36m-manylinux1_x86_64.whl Collecting pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 (from matplotlib) Using cached https://files.pythonhosted.org/packages/5d/bc/1e58593167fade7b544bfe9502a26dc860940a79ab306e651e7f13be68c2/pyparsing-2.4.6-py2.py3-none-any.whl Collecting cycler>=0.10 (from matplotlib) Using cached https://files.pythonhosted.org/packages/f7/d2/e07d3ebb2bd7af696440ce7e754c59dd546ffe1bbe732c8ab68b9c834e61/cycler-0.10.0-py2.py3-none-any.whl Collecting pytz>=2017.2 (from pandas>=0.25->xarray) Using cached https://files.pythonhosted.org/packages/e7/f9/f0b53f88060247251bf481fa6ea62cd0d25bf1b11a87888e53ce5b7c8ad2/pytz-2019.3-py2.py3-none-any.whl Collecting six>=1.5 (from python-dateutil>=2.1->matplotlib) Using cached https://files.pythonhosted.org/packages/65/eb/1f97cb97bfc2390a276969c6fae16075da282f5058082d4cb10c6c5c1dba/six-1.14.0-py2.py3-none-any.whl Installing collected packages: numpy, setuptools, pytz, six, python-dateutil, pandas, xarray, kiwisolver, pyparsing, cycler, matplotlib Successfully installed cycler-0.10.0 kiwisolver-1.1.0 matplotlib-3.2.1 numpy-1.18.2 pandas-1.0.3 pyparsing-2.4.6 python-dateutil-2.8.1 pytz-2019.3 setuptools-46.1.3 six-1.14.0 xarray-0.15.1
Install and import cf-json extension to xarray which will be used to turn netCDF-like json as returned by the API into an xarray Dataset
In [2]:
!{sys.executable} -m pip install git+ssh://git@github.com/metocean/cf-json@v0.3.3 from cfjson.xrdataset import CFJSONinterface
Collecting git+ssh://git@github.com/metocean/cf-json@v0.3.3 Cloning ssh://git@github.com/metocean/cf-json (to v0.3.3) to /tmp/pip-ne5mykz6-build Installing collected packages: cfjson Running setup.py install for cfjson ... done Successfully installed cfjson-0.3.3
Import request library to query the API and path module of os package to help building url
In [3]:
import requests from os import path
Definition of connector
Declare a new exception class that can be used to identify any exception raised by the connector
In [4]:
class MetOceanObsAPIException(Exception): def __init__(self, message, obj=None, obj_name=None, status=400): self.message = message self.obj = obj self.obj_name = obj_name self.status = status super(ThreddsServerConnector, self).__init__(message)
Definition of connector
In [5]:
class MetOceanObsAPIConnector(object): """ A class used to connect one of MetOcean Solution's instances of their observation API. Attributes ---------- root_url : str The root url of the service Methods ------- get_station_metadata(station_id) return the metadata associated with a given observation station get_data() return observation data retrieved from the API in the form of a xarray dataset object """ def __init__(self, root_url="https://observations-open.metoceanapi.com", username=None, password=None, api_key=None): """ Initialise the connector Parameters ---------- root_url : str The url of the observation API to connect to. (default is https://observations-open.metoceanapi.com) username : str The username to use to connect the API password : str The password to use to connect the API api_key : str The api_key to use to connect the API Raises ------ NotImplementedError If any of the authentication parameters is set. """ self.root_url = path.join(root_url, 'api/stations') if username or password or api_key: raise NotImplementedError("Secure authentication is yet to be implemented for the connector") def get_station_metadata(self, station_id): """ Get the metadata associated with a given observation station Parameters ---------- station_id : str The id of the station to return the metadata for. Raises ------ MetOceanObsAPIException If query did not return with 200 status code Returns ------- dict A dictionary containing the metadata associated with the station """ ## Build query url url = path.join(self.root_url, station_id) ## Do request res = requests.get(url) ## Check request successed. If not raise exception if res.status_code != 200: raise MetOceanObsAPIException("getstation_metadata query failed with status code {1} and reasons: %s"\ .format(res.status_code,\ res.text)) ## Decode JSON response and return return res.json() def get_data(self, station_id, time=None, variables=None, max_nrecords=100): """ Get the metadata associated with a given observation station Parameters ---------- station_id : str The id of the station to return the metadata for. time : str ISO8601 time interval with double dash option and empty start/end allowed used to filter the data. Valid exemples are: time=2007-03-01T13:00:00Z--2008-05-11T15:30:00Z time=2007-03-01T13:00:00Z-- time=--2008-05-11T15:30:00Z variables : list List of strings containing the name of the variables to retrieve. Return all by default max_nrecords : int The maximum number of records to retrieve (default is 100) Raises ------ MetOceanObsAPIException If query did not return with 200 status code Returns ------- xarray dataset A xarray dataset containing the data for the station """ ## Build full url to query records url = path.join(self.root_url, station_id, 'records') ## Build dictionary with query parameters params = {} params['time'] = time params['vars'] = variables params['limit'] = max_nrecords ## Excecute request res = requests.get(url, headers={'accept': 'application/vnd.metocean.cf+json'}, params=params) ## Check for success if res.status_code != 200: raise MetOceanObsAPIException("Get data query failed with status code {1} and reasons: %s"\ .format(res.status_code,\ res.text)) ## Turn data into xarray dataset dset = xarray.Dataset() dset.cfjson.from_json(res.json()) ## Return xarray dataset return dset
Instantiate new connector and retrieve metadata for the station of id 'ciwrb'
In [6]:
obs_api_connector = MetOceanObsAPIConnector()
metadata = obs_api_connector.get_station_metadata(station_id='ciwrb')
print(metadata)
{'id': 'ciwrb', 'name': 'ciwrb', 'tags': ['wave'], 'description': '', 'provider': 'metocean', 'level': 0.0, 'dataset': 'socean', 'coordinates': 'POINT (169.04916 -52.76000000000001)', 'latitude': -52.76000000000001, 'longitude': 169.04915999999997, 'public': True, 'is_active': False, 'last_record_at': '2019-06-19T18:00:00Z'}
For now the metadata available is limited and some metadata like the variables provided for a given station are not available. That information can be retrieved by first querying for a sample of the data.
In [7]:
dset = obs_api_connector.get_data(station_id='ciwrb',
max_nrecords=3)From the sample result, the names of the variables can be listed:
In [8]:
for (var_name, var) in dset.data_vars.items(): print(var.attrs['standard_name'],'-->', var_name)
latitude --> latitude
longitude --> longitude
altitude --> level
sea_surface_wave_period_at_variance_spectral_density_maximum --> tp
sea_surface_wave_significant_height --> hs
sea_surface_wave_maximum_height --> hmax
sea_surface_wave_from_direction_at_variance_spectral_density_maximum --> dpmNow we can query for specific variables only and a set time range
In [9]:
dset = obs_api_connector.get_data(station_id='ciwrb', time='2019-06-18T15:00:00Z--2019-06-19T21:00:00Z', variables=['hs', 'tp'], max_nrecords=300) print("Retrieved {0} records for station ciwrb".format(len(dset.time)))
Retrieved 9 records for station ciwrb
Now we can do the same for a weather station. We retrieve temperature (tmp) since 2020-01-21 19:00:00Z and we get at most 200 records
In [10]:
dset = obs_api_connector.get_data(station_id='tu0fknyk', time=['2020-01-21 19:00:00Z--'], max_nrecords=200) print("Retrieved {0} records for station tu0fknyk".format(len(dset.time)))
Retrieved 200 records for station tu0fknyk
Now lets plot the data using matplotlib
In [11]:
%matplotlib inline import matplotlib.pyplot as plt from matplotlib.dates import date2num plt.xlabel('time') plt.ylabel('Temperature (degree Celcius)') plt.plot_date(date2num(dset.time), dset.tmp.data)
Out[11]:
[<matplotlib.lines.Line2D at 0x14c3179fb2e8>]
Simple numerical data correction example
Now we are going to put together a toy example aiming at correcting numerical data using observation data. That kind of approach could be used in the context of statistical downscaling.
Note that this is a toy example aimed at demonstrating a workflow and one added constraint for the demonstration was that it had to use a neural network model.
The amount of data used here is tiny and clearly not enough to build something scientifically robust. Also the model is only suitable in that it is aimed at working on sequences of data, we did not investigate scientific suitability and it is likely there are simpler and more suitable options.
The data used here is the 2 metres temperature from the nowcast for the last 9 days from the 0.25 degree GFS model at the same location as that of the observation station tu0fknyk.
In [12]:
from datetime import datetime from dateutil.relativedelta import relativedelta import numpy as np
Here is a useful function that converts the datetime format used by xarray to the "standard" python datetimes.
In [13]:
def datetime64_to_datetime(dd): return datetime.utcfromtimestamp(round(dd.data.astype(int) * 1e-9))
Get the longitude/latitude coordinates of the observation point
In [14]:
lon0 = dset.longitude.data[0]
lat0 = dset.latitude.data[0]Now load one of the GFS dataset to identify the nearest grid point to the observation station
In [76]:
now = datetime.utcnow()
gfs = xarray.open_dataset('https://nomads.ncep.noaa.gov:9090/dods/gfs_0p25_1hr/gfs%04d%02d%02d/gfs_0p25_1hr_00z'%(now.year, now.month, now.day))In [77]:
ilon = np.argmin(np.abs(gfs.lon.data-lon0))
ilat = np.argmin(np.abs(gfs.lat.data-lat0))
print("Approximating longitude",lon0,"by",gfs.lon.data[ilon])
print("Approximating latitude",lat0,"by",gfs.lat.data[ilat])
print(ilon, ilat)Approximating longitude 174.971 by 175.0 Approximating latitude -37.03 by -37.0 700 212
For the last 9 days, download all forecast guidances (cycles 0,6,12 and 18) available and keep the 2 metre temperature variable at the point of interest for the first 6 hours (nowcast).
In [18]:
now = datetime.utcnow()
t = datetime(now.year, now.month, now.day) - relativedelta(days=9)
data = []
files = []
while t < now:
for cycle in [0,6,12,18]:
try:
gfs = xarray.open_dataset('https://nomads.ncep.noaa.gov:9090/dods/gfs_0p25_1hr/gfs%04d%02d%02d/gfs_0p25_1hr_%02dz'%(t.year,t.month,t.day,cycle))
print(gfs.time[0].data)
files.append(gfs)
data.append(gfs.tmp2m[:6,ilat,ilon])
except:
pass
t = t + relativedelta(days=1)2020-03-21T00:00:00.000000000 2020-03-21T06:00:00.000000000 2020-03-21T12:00:00.000000000 2020-03-21T18:00:00.000000000 2020-03-22T00:00:00.000000000 2020-03-22T06:00:00.000000000 2020-03-22T12:00:00.000000000 2020-03-22T18:00:00.000000000 2020-03-23T00:00:00.000000000 2020-03-23T06:00:00.000000000 2020-03-23T12:00:00.000000000 2020-03-23T18:00:00.000000000 2020-03-24T00:00:00.000000000 2020-03-24T06:00:00.000000000 2020-03-24T12:00:00.000000000 2020-03-24T18:00:00.000000000 2020-03-25T00:00:00.000000000 2020-03-25T06:00:00.000000000 2020-03-25T12:00:00.000000000 2020-03-25T18:00:00.000000000 2020-03-26T00:00:00.000000000 2020-03-26T06:00:00.000000000 2020-03-26T12:00:00.000000000 2020-03-26T18:00:00.000000000 2020-03-27T00:00:00.000000000 2020-03-27T06:00:00.000000000 2020-03-27T12:00:00.000000000 2020-03-27T18:00:00.000000000 2020-03-28T00:00:00.000000000 2020-03-28T06:00:00.000000000 2020-03-28T12:00:00.000000000 2020-03-28T18:00:00.000000000 2020-03-29T00:00:00.000000000 2020-03-29T06:00:00.000000000 2020-03-29T12:00:00.000000000 2020-03-29T18:00:00.000000000 2020-03-30T00:00:00.000000000 2020-03-30T06:00:00.000000000 2020-03-30T12:00:00.000000000
Concatenate all the 6 hours segments into a single one
In [19]:
nowcast = xarray.concat(data,dim='time')
Now we get all the observation available since the start of the nowcast
In [20]:
t0 = datetime64_to_datetime(nowcast.time[0])
obs = obs_api_connector.get_data(station_id='tu0fknyk',
time=['%04d-%02d-%02d %02d:00:00Z--'%(t0.year, t0.month, t0.day, t0.hour)],
max_nrecords=1000)As the observation API returns the most recent recoords first, we sort them by ascending time.
In [21]:
obs = obs.sortby('time', ascending=True)Now we match the data. For each observation that has a corresponding time-stamp in the nowcast put data keep the data.
In [22]:
obs_data = [] nowcast_data = [] time = [] ## Parse datetime for nowcast nowcast_time = np.array([datetime64_to_datetime(t) for t in nowcast.time]) for it, t in enumerate(obs.time[:]): ## Check if match match = np.where(nowcast_time == datetime64_to_datetime(t)) if len(match[0]) == 1: obs_data.append(obs.tmp.data[it]) nowcast_data.append(nowcast[match[0]].data[0]) time.append(datetime64_to_datetime(t)) ## Turn list into numpy array (nicer to work with) obs_data = np.array(obs_data) ## Do also conversion from Kelvin to Celcius nowcast_data = np.array(nowcast_data)-273.18 time = np.array(time)
Now let's plot the retrieved data. There does not seem to be much if any bias but it looks like the amplitude of the daily variations in the observations is slightly larger than that of the nowcast data so maybe we can correct for that.
In [23]:
%matplotlib inline plt.plot_date(date2num(time), obs_data, label='Observations') plt.plot_date(date2num(time), nowcast_data, label='Nowcast') plt.legend()
Out[23]:
<matplotlib.legend.Legend at 0x14c3112d52b0>
We will have a go at using a simple LSTM model built using Keras with a tensorflow backend. We assume that the image/environment we use here already has CUDA/CUDNN/Tensorflow installed. We just add Keras.
In [28]:
!{sys.executable} -m pip install keras
Collecting keras Using cached https://files.pythonhosted.org/packages/ad/fd/6bfe87920d7f4fd475acd28500a42482b6b84479832bdc0fe9e589a60ceb/Keras-2.3.1-py2.py3-none-any.whl Collecting h5py (from keras) Using cached https://files.pythonhosted.org/packages/60/06/cafdd44889200e5438b897388f3075b52a8ef01f28a17366d91de0fa2d05/h5py-2.10.0-cp36-cp36m-manylinux1_x86_64.whl Collecting keras-preprocessing>=1.0.5 (from keras) Using cached https://files.pythonhosted.org/packages/28/6a/8c1f62c37212d9fc441a7e26736df51ce6f0e38455816445471f10da4f0a/Keras_Preprocessing-1.1.0-py2.py3-none-any.whl Collecting six>=1.9.0 (from keras) Using cached https://files.pythonhosted.org/packages/65/eb/1f97cb97bfc2390a276969c6fae16075da282f5058082d4cb10c6c5c1dba/six-1.14.0-py2.py3-none-any.whl Collecting pyyaml (from keras) Collecting scipy>=0.14 (from keras) Using cached https://files.pythonhosted.org/packages/dc/29/162476fd44203116e7980cfbd9352eef9db37c49445d1fec35509022f6aa/scipy-1.4.1-cp36-cp36m-manylinux1_x86_64.whl Collecting numpy>=1.9.1 (from keras) Using cached https://files.pythonhosted.org/packages/07/08/a549ba8b061005bb629b76adc000f3caaaf881028b963c2e18f811c6edc1/numpy-1.18.2-cp36-cp36m-manylinux1_x86_64.whl Collecting keras-applications>=1.0.6 (from keras) Using cached https://files.pythonhosted.org/packages/71/e3/19762fdfc62877ae9102edf6342d71b28fbfd9dea3d2f96a882ce099b03f/Keras_Applications-1.0.8-py3-none-any.whl Installing collected packages: six, numpy, h5py, keras-preprocessing, pyyaml, scipy, keras-applications, keras Successfully installed h5py-2.10.0 keras-2.3.1 keras-applications-1.0.8 keras-preprocessing-1.1.0 numpy-1.18.2 pyyaml-5.3.1 scipy-1.4.1 six-1.14.0
First we prepare the data to model. We will use sequences of the 15 consecutive nowcast points and attempt at predicting the difference between the observations and the nowcast.
In [24]:
x = np.array([nowcast_data[i:i+15] for i in range(len(nowcast_data)-15)]) y = np.array([obs_data[i:i+15][-1]-nowcast_data[i:i+15][-1] for i in range(len(obs_data)-15)]) ## Needs to be of shape (nsamples, sequence_length, nfeatures) x = x.reshape(x.shape[0], x.shape[1], 1)
We normalise the input data to the neural network
In [27]:
xmin = x.min() xmax = x.max() x = (x-xmin)/(xmax-xmin) print(xmin, xmax)
8.8012085 24.061737
Now we load leras and build the model
In [67]:
import keras from keras.models import Sequential, Model from keras.layers import Dense, LSTM, Input neurons = 10 batch_size = 32 nb_epoch = 1000
In [68]:
ip = Input((x.shape[1], x.shape[2])) xx = LSTM(neurons, stateful=False)(ip) out = Dense(1)(xx) model = Model(ip, out) model.compile(loss='mean_squared_error', optimizer='adam')
We train the model on all data except for the last 50 samples
In [69]:
model.fit(x[:-50], y[:-50], epochs=nb_epoch, batch_size=batch_size, verbose=1, shuffle=False)
Epoch 1/1000 168/168 [==============================] - 0s 2ms/step - loss: 2.5169 Epoch 2/1000 168/168 [==============================] - 0s 114us/step - loss: 2.4623 Epoch 3/1000 168/168 [==============================] - 0s 115us/step - loss: 2.4153 Epoch 4/1000 168/168 [==============================] - 0s 125us/step - loss: 2.3740 Epoch 5/1000 168/168 [==============================] - 0s 101us/step - loss: 2.3374 Epoch 6/1000 168/168 [==============================] - 0s 138us/step - loss: 2.3045 Epoch 7/1000 168/168 [==============================] - 0s 111us/step - loss: 2.2747 Epoch 8/1000 168/168 [==============================] - 0s 130us/step - loss: 2.2474 Epoch 9/1000 168/168 [==============================] - 0s 116us/step - loss: 2.2220 Epoch 10/1000 168/168 [==============================] - 0s 108us/step - loss: 2.1983 Epoch 11/1000 168/168 [==============================] - 0s 107us/step - loss: 2.1762 Epoch 12/1000 168/168 [==============================] - 0s 108us/step - loss: 2.1557 Epoch 13/1000 168/168 [==============================] - 0s 196us/step - loss: 2.1367 Epoch 14/1000 168/168 [==============================] - 0s 115us/step - loss: 2.1194 Epoch 15/1000 168/168 [==============================] - 0s 120us/step - loss: 2.1040 Epoch 16/1000 168/168 [==============================] - 0s 115us/step - loss: 2.0907 Epoch 17/1000 168/168 [==============================] - 0s 117us/step - loss: 2.0796 Epoch 18/1000 168/168 [==============================] - 0s 112us/step - loss: 2.0708 Epoch 19/1000 168/168 [==============================] - 0s 103us/step - loss: 2.0640 Epoch 20/1000 168/168 [==============================] - 0s 96us/step - loss: 2.0589 Epoch 21/1000 168/168 [==============================] - 0s 110us/step - loss: 2.0548 Epoch 22/1000 168/168 [==============================] - 0s 104us/step - loss: 2.0512 Epoch 23/1000 168/168 [==============================] - 0s 109us/step - loss: 2.0476 Epoch 24/1000 168/168 [==============================] - 0s 123us/step - loss: 2.0437 Epoch 25/1000 168/168 [==============================] - 0s 107us/step - loss: 2.0393 Epoch 26/1000 168/168 [==============================] - 0s 121us/step - loss: 2.0346 Epoch 27/1000 168/168 [==============================] - 0s 105us/step - loss: 2.0296 Epoch 28/1000 168/168 [==============================] - 0s 106us/step - loss: 2.0244 Epoch 29/1000 168/168 [==============================] - 0s 125us/step - loss: 2.0191 Epoch 30/1000 168/168 [==============================] - 0s 104us/step - loss: 2.0137 Epoch 31/1000 168/168 [==============================] - 0s 128us/step - loss: 2.0084 Epoch 32/1000 168/168 [==============================] - 0s 141us/step - loss: 2.0030 Epoch 33/1000 168/168 [==============================] - 0s 114us/step - loss: 1.9976 Epoch 34/1000 168/168 [==============================] - 0s 134us/step - loss: 1.9921 Epoch 35/1000 168/168 [==============================] - 0s 108us/step - loss: 1.9866 Epoch 36/1000 168/168 [==============================] - 0s 110us/step - loss: 1.9811 Epoch 37/1000 168/168 [==============================] - 0s 97us/step - loss: 1.9755 Epoch 38/1000 168/168 [==============================] - 0s 115us/step - loss: 1.9699 Epoch 39/1000 168/168 [==============================] - 0s 145us/step - loss: 1.9642 Epoch 40/1000 168/168 [==============================] - 0s 112us/step - loss: 1.9585 Epoch 41/1000 168/168 [==============================] - 0s 108us/step - loss: 1.9527 Epoch 42/1000 168/168 [==============================] - 0s 117us/step - loss: 1.9469 Epoch 43/1000 168/168 [==============================] - 0s 141us/step - loss: 1.9411 Epoch 44/1000 168/168 [==============================] - 0s 108us/step - loss: 1.9352 Epoch 45/1000 168/168 [==============================] - 0s 142us/step - loss: 1.9293 Epoch 46/1000 168/168 [==============================] - 0s 97us/step - loss: 1.9233 Epoch 47/1000 168/168 [==============================] - 0s 129us/step - loss: 1.9174 Epoch 48/1000 168/168 [==============================] - 0s 122us/step - loss: 1.9114 Epoch 49/1000 168/168 [==============================] - 0s 122us/step - loss: 1.9055 Epoch 50/1000 168/168 [==============================] - 0s 108us/step - loss: 1.8996 Epoch 51/1000 168/168 [==============================] - 0s 117us/step - loss: 1.8938 Epoch 52/1000 168/168 [==============================] - 0s 125us/step - loss: 1.8880 Epoch 53/1000 168/168 [==============================] - 0s 110us/step - loss: 1.8822 Epoch 54/1000 168/168 [==============================] - 0s 114us/step - loss: 1.8766 Epoch 55/1000 168/168 [==============================] - 0s 109us/step - loss: 1.8710 Epoch 56/1000 168/168 [==============================] - 0s 108us/step - loss: 1.8655 Epoch 57/1000 168/168 [==============================] - 0s 125us/step - loss: 1.8602 Epoch 58/1000 168/168 [==============================] - 0s 106us/step - loss: 1.8550 Epoch 59/1000 168/168 [==============================] - 0s 122us/step - loss: 1.8500 Epoch 60/1000 168/168 [==============================] - 0s 107us/step - loss: 1.8451 Epoch 61/1000 168/168 [==============================] - 0s 99us/step - loss: 1.8404 Epoch 62/1000 168/168 [==============================] - 0s 105us/step - loss: 1.8358 Epoch 63/1000 168/168 [==============================] - 0s 98us/step - loss: 1.8314 Epoch 64/1000 168/168 [==============================] - 0s 115us/step - loss: 1.8273 Epoch 65/1000 168/168 [==============================] - 0s 110us/step - loss: 1.8232 Epoch 66/1000 168/168 [==============================] - 0s 114us/step - loss: 1.8194 Epoch 67/1000 168/168 [==============================] - 0s 115us/step - loss: 1.8157 Epoch 68/1000 168/168 [==============================] - 0s 118us/step - loss: 1.8122 Epoch 69/1000 168/168 [==============================] - 0s 135us/step - loss: 1.8088 Epoch 70/1000 168/168 [==============================] - 0s 107us/step - loss: 1.8055 Epoch 71/1000 168/168 [==============================] - 0s 109us/step - loss: 1.8024 Epoch 72/1000 168/168 [==============================] - 0s 121us/step - loss: 1.7994 Epoch 73/1000 168/168 [==============================] - 0s 112us/step - loss: 1.7965 Epoch 74/1000 168/168 [==============================] - 0s 103us/step - loss: 1.7937 Epoch 75/1000 168/168 [==============================] - 0s 127us/step - loss: 1.7909 Epoch 76/1000 168/168 [==============================] - 0s 111us/step - loss: 1.7882 Epoch 77/1000 168/168 [==============================] - 0s 101us/step - loss: 1.7856 Epoch 78/1000 168/168 [==============================] - 0s 109us/step - loss: 1.7831 Epoch 79/1000 168/168 [==============================] - 0s 111us/step - loss: 1.7805 Epoch 80/1000 168/168 [==============================] - 0s 104us/step - loss: 1.7781 Epoch 81/1000 168/168 [==============================] - 0s 108us/step - loss: 1.7756 Epoch 82/1000 168/168 [==============================] - 0s 107us/step - loss: 1.7732 Epoch 83/1000 168/168 [==============================] - 0s 136us/step - loss: 1.7708 Epoch 84/1000 168/168 [==============================] - 0s 115us/step - loss: 1.7684 Epoch 85/1000 168/168 [==============================] - 0s 149us/step - loss: 1.7661 Epoch 86/1000 168/168 [==============================] - 0s 130us/step - loss: 1.7637 Epoch 87/1000 168/168 [==============================] - 0s 104us/step - loss: 1.7613 Epoch 88/1000 168/168 [==============================] - 0s 102us/step - loss: 1.7590 Epoch 89/1000 168/168 [==============================] - 0s 154us/step - loss: 1.7567 Epoch 90/1000 168/168 [==============================] - 0s 100us/step - loss: 1.7543 Epoch 91/1000 168/168 [==============================] - 0s 106us/step - loss: 1.7519 Epoch 92/1000 168/168 [==============================] - 0s 102us/step - loss: 1.7496 Epoch 93/1000 168/168 [==============================] - 0s 108us/step - loss: 1.7472 Epoch 94/1000 168/168 [==============================] - 0s 108us/step - loss: 1.7448 Epoch 95/1000 168/168 [==============================] - 0s 106us/step - loss: 1.7424 Epoch 96/1000 168/168 [==============================] - 0s 110us/step - loss: 1.7400 Epoch 97/1000 168/168 [==============================] - 0s 106us/step - loss: 1.7376 Epoch 98/1000 168/168 [==============================] - 0s 120us/step - loss: 1.7351 Epoch 99/1000 168/168 [==============================] - 0s 102us/step - loss: 1.7327 Epoch 100/1000 168/168 [==============================] - 0s 100us/step - loss: 1.7302 Epoch 101/1000 168/168 [==============================] - 0s 111us/step - loss: 1.7276 Epoch 102/1000 168/168 [==============================] - 0s 118us/step - loss: 1.7251 Epoch 103/1000 168/168 [==============================] - 0s 104us/step - loss: 1.7225 Epoch 104/1000 168/168 [==============================] - 0s 108us/step - loss: 1.7199 Epoch 105/1000 168/168 [==============================] - 0s 107us/step - loss: 1.7173 Epoch 106/1000 168/168 [==============================] - 0s 109us/step - loss: 1.7146 Epoch 107/1000 168/168 [==============================] - 0s 108us/step - loss: 1.7119 Epoch 108/1000 168/168 [==============================] - 0s 109us/step - loss: 1.7091 Epoch 109/1000 168/168 [==============================] - 0s 130us/step - loss: 1.7063 Epoch 110/1000 168/168 [==============================] - 0s 103us/step - loss: 1.7035 Epoch 111/1000 168/168 [==============================] - 0s 103us/step - loss: 1.7006 Epoch 112/1000 168/168 [==============================] - 0s 118us/step - loss: 1.6977 Epoch 113/1000 168/168 [==============================] - 0s 93us/step - loss: 1.6947 Epoch 114/1000 168/168 [==============================] - 0s 119us/step - loss: 1.6917 Epoch 115/1000 168/168 [==============================] - 0s 127us/step - loss: 1.6886 Epoch 116/1000 168/168 [==============================] - 0s 119us/step - loss: 1.6854 Epoch 117/1000 168/168 [==============================] - 0s 107us/step - loss: 1.6822 Epoch 118/1000 168/168 [==============================] - 0s 104us/step - loss: 1.6789 Epoch 119/1000 168/168 [==============================] - 0s 117us/step - loss: 1.6756 Epoch 120/1000 168/168 [==============================] - 0s 108us/step - loss: 1.6722 Epoch 121/1000 168/168 [==============================] - 0s 124us/step - loss: 1.6687 Epoch 122/1000 168/168 [==============================] - 0s 101us/step - loss: 1.6651 Epoch 123/1000 168/168 [==============================] - 0s 98us/step - loss: 1.6615 Epoch 124/1000 168/168 [==============================] - 0s 118us/step - loss: 1.6578 Epoch 125/1000 168/168 [==============================] - 0s 105us/step - loss: 1.6540 Epoch 126/1000 168/168 [==============================] - 0s 104us/step - loss: 1.6502 Epoch 127/1000 168/168 [==============================] - 0s 100us/step - loss: 1.6462 Epoch 128/1000 168/168 [==============================] - 0s 116us/step - loss: 1.6423 Epoch 129/1000 168/168 [==============================] - 0s 105us/step - loss: 1.6382 Epoch 130/1000 168/168 [==============================] - 0s 112us/step - loss: 1.6341 Epoch 131/1000 168/168 [==============================] - 0s 101us/step - loss: 1.6300 Epoch 132/1000 168/168 [==============================] - 0s 105us/step - loss: 1.6258 Epoch 133/1000 168/168 [==============================] - 0s 99us/step - loss: 1.6216 Epoch 134/1000 168/168 [==============================] - 0s 101us/step - loss: 1.6174 Epoch 135/1000 168/168 [==============================] - 0s 102us/step - loss: 1.6132 Epoch 136/1000 168/168 [==============================] - 0s 111us/step - loss: 1.6090 Epoch 137/1000 168/168 [==============================] - 0s 110us/step - loss: 1.6049 Epoch 138/1000 168/168 [==============================] - 0s 99us/step - loss: 1.6007 Epoch 139/1000 168/168 [==============================] - 0s 109us/step - loss: 1.5967 Epoch 140/1000 168/168 [==============================] - 0s 105us/step - loss: 1.5926 Epoch 141/1000 168/168 [==============================] - 0s 129us/step - loss: 1.5887 Epoch 142/1000 168/168 [==============================] - 0s 104us/step - loss: 1.5848 Epoch 143/1000 168/168 [==============================] - 0s 149us/step - loss: 1.5810 Epoch 144/1000 168/168 [==============================] - 0s 110us/step - loss: 1.5773 Epoch 145/1000 168/168 [==============================] - 0s 120us/step - loss: 1.5737 Epoch 146/1000 168/168 [==============================] - 0s 138us/step - loss: 1.5702 Epoch 147/1000 168/168 [==============================] - 0s 129us/step - loss: 1.5668 Epoch 148/1000 168/168 [==============================] - 0s 111us/step - loss: 1.5635 Epoch 149/1000 168/168 [==============================] - 0s 107us/step - loss: 1.5603 Epoch 150/1000 168/168 [==============================] - 0s 117us/step - loss: 1.5572 Epoch 151/1000 168/168 [==============================] - 0s 137us/step - loss: 1.5542 Epoch 152/1000 168/168 [==============================] - 0s 109us/step - loss: 1.5514 Epoch 153/1000 168/168 [==============================] - 0s 103us/step - loss: 1.5486 Epoch 154/1000 168/168 [==============================] - 0s 107us/step - loss: 1.5459 Epoch 155/1000 168/168 [==============================] - 0s 104us/step - loss: 1.5432 Epoch 156/1000 168/168 [==============================] - 0s 97us/step - loss: 1.5407 Epoch 157/1000 168/168 [==============================] - 0s 106us/step - loss: 1.5382 Epoch 158/1000 168/168 [==============================] - 0s 134us/step - loss: 1.5358 Epoch 159/1000 168/168 [==============================] - 0s 118us/step - loss: 1.5334 Epoch 160/1000 168/168 [==============================] - 0s 120us/step - loss: 1.5311 Epoch 161/1000 168/168 [==============================] - 0s 104us/step - loss: 1.5289 Epoch 162/1000 168/168 [==============================] - 0s 99us/step - loss: 1.5267 Epoch 163/1000 168/168 [==============================] - 0s 126us/step - loss: 1.5245 Epoch 164/1000 168/168 [==============================] - 0s 112us/step - loss: 1.5224 Epoch 165/1000 168/168 [==============================] - 0s 110us/step - loss: 1.5204 Epoch 166/1000 168/168 [==============================] - 0s 104us/step - loss: 1.5183 Epoch 167/1000 168/168 [==============================] - 0s 102us/step - loss: 1.5163 Epoch 168/1000 168/168 [==============================] - 0s 103us/step - loss: 1.5143 Epoch 169/1000 168/168 [==============================] - 0s 102us/step - loss: 1.5124 Epoch 170/1000 168/168 [==============================] - 0s 108us/step - loss: 1.5105 Epoch 171/1000 168/168 [==============================] - 0s 112us/step - loss: 1.5086 Epoch 172/1000 168/168 [==============================] - 0s 135us/step - loss: 1.5067 Epoch 173/1000 168/168 [==============================] - 0s 102us/step - loss: 1.5048 Epoch 174/1000 168/168 [==============================] - 0s 119us/step - loss: 1.5030 Epoch 175/1000 168/168 [==============================] - 0s 104us/step - loss: 1.5012 Epoch 176/1000 168/168 [==============================] - 0s 100us/step - loss: 1.4994 Epoch 177/1000 168/168 [==============================] - 0s 111us/step - loss: 1.4976 Epoch 178/1000 168/168 [==============================] - 0s 109us/step - loss: 1.4959 Epoch 179/1000 168/168 [==============================] - 0s 101us/step - loss: 1.4941 Epoch 180/1000 168/168 [==============================] - 0s 115us/step - loss: 1.4924 Epoch 181/1000 168/168 [==============================] - 0s 105us/step - loss: 1.4906 Epoch 182/1000 168/168 [==============================] - 0s 107us/step - loss: 1.4889 Epoch 183/1000 168/168 [==============================] - 0s 122us/step - loss: 1.4872 Epoch 184/1000 168/168 [==============================] - 0s 130us/step - loss: 1.4855 Epoch 185/1000 168/168 [==============================] - 0s 107us/step - loss: 1.4838 Epoch 186/1000 168/168 [==============================] - 0s 105us/step - loss: 1.4821 Epoch 187/1000 168/168 [==============================] - 0s 105us/step - loss: 1.4804 Epoch 188/1000 168/168 [==============================] - 0s 123us/step - loss: 1.4787 Epoch 189/1000 168/168 [==============================] - 0s 151us/step - loss: 1.4770 Epoch 190/1000 168/168 [==============================] - 0s 113us/step - loss: 1.4754 Epoch 191/1000 168/168 [==============================] - 0s 106us/step - loss: 1.4737 Epoch 192/1000 168/168 [==============================] - 0s 102us/step - loss: 1.4720 Epoch 193/1000 168/168 [==============================] - 0s 120us/step - loss: 1.4703 Epoch 194/1000 168/168 [==============================] - 0s 109us/step - loss: 1.4687 Epoch 195/1000 168/168 [==============================] - 0s 99us/step - loss: 1.4670 Epoch 196/1000 168/168 [==============================] - 0s 122us/step - loss: 1.4653 Epoch 197/1000 168/168 [==============================] - 0s 106us/step - loss: 1.4636 Epoch 198/1000 168/168 [==============================] - 0s 100us/step - loss: 1.4619 Epoch 199/1000 168/168 [==============================] - 0s 114us/step - loss: 1.4602 Epoch 200/1000 168/168 [==============================] - 0s 98us/step - loss: 1.4585 Epoch 201/1000 168/168 [==============================] - 0s 109us/step - loss: 1.4568 Epoch 202/1000 168/168 [==============================] - 0s 104us/step - loss: 1.4550 Epoch 203/1000 168/168 [==============================] - 0s 107us/step - loss: 1.4533 Epoch 204/1000 168/168 [==============================] - 0s 106us/step - loss: 1.4516 Epoch 205/1000 168/168 [==============================] - 0s 116us/step - loss: 1.4498 Epoch 206/1000 168/168 [==============================] - 0s 111us/step - loss: 1.4481 Epoch 207/1000 168/168 [==============================] - 0s 103us/step - loss: 1.4463 Epoch 208/1000 168/168 [==============================] - 0s 114us/step - loss: 1.4445 Epoch 209/1000 168/168 [==============================] - 0s 110us/step - loss: 1.4427 Epoch 210/1000 168/168 [==============================] - 0s 115us/step - loss: 1.4409 Epoch 211/1000 168/168 [==============================] - 0s 136us/step - loss: 1.4391 Epoch 212/1000 168/168 [==============================] - 0s 101us/step - loss: 1.4372 Epoch 213/1000 168/168 [==============================] - 0s 119us/step - loss: 1.4354 Epoch 214/1000 168/168 [==============================] - 0s 113us/step - loss: 1.4335 Epoch 215/1000 168/168 [==============================] - 0s 105us/step - loss: 1.4316 Epoch 216/1000 168/168 [==============================] - 0s 104us/step - loss: 1.4297 Epoch 217/1000 168/168 [==============================] - 0s 95us/step - loss: 1.4278 Epoch 218/1000 168/168 [==============================] - 0s 99us/step - loss: 1.4259 Epoch 219/1000 168/168 [==============================] - 0s 132us/step - loss: 1.4240 Epoch 220/1000 168/168 [==============================] - 0s 109us/step - loss: 1.4220 Epoch 221/1000 168/168 [==============================] - 0s 100us/step - loss: 1.4201 Epoch 222/1000 168/168 [==============================] - 0s 106us/step - loss: 1.4181 Epoch 223/1000 168/168 [==============================] - 0s 151us/step - loss: 1.4161 Epoch 224/1000 168/168 [==============================] - 0s 115us/step - loss: 1.4141 Epoch 225/1000 168/168 [==============================] - 0s 107us/step - loss: 1.4121 Epoch 226/1000 168/168 [==============================] - 0s 97us/step - loss: 1.4101 Epoch 227/1000 168/168 [==============================] - 0s 131us/step - loss: 1.4081 Epoch 228/1000 168/168 [==============================] - 0s 127us/step - loss: 1.4060 Epoch 229/1000 168/168 [==============================] - 0s 98us/step - loss: 1.4040 Epoch 230/1000 168/168 [==============================] - 0s 109us/step - loss: 1.4019 Epoch 231/1000 168/168 [==============================] - 0s 112us/step - loss: 1.3998 Epoch 232/1000 168/168 [==============================] - 0s 101us/step - loss: 1.3977 Epoch 233/1000 168/168 [==============================] - 0s 105us/step - loss: 1.3956 Epoch 234/1000 168/168 [==============================] - 0s 110us/step - loss: 1.3935 Epoch 235/1000 168/168 [==============================] - 0s 98us/step - loss: 1.3914 Epoch 236/1000 168/168 [==============================] - 0s 118us/step - loss: 1.3893 Epoch 237/1000 168/168 [==============================] - 0s 143us/step - loss: 1.3871 Epoch 238/1000 168/168 [==============================] - 0s 114us/step - loss: 1.3850 Epoch 239/1000 168/168 [==============================] - 0s 105us/step - loss: 1.3828 Epoch 240/1000 168/168 [==============================] - 0s 104us/step - loss: 1.3806 Epoch 241/1000 168/168 [==============================] - 0s 107us/step - loss: 1.3784 Epoch 242/1000 168/168 [==============================] - 0s 103us/step - loss: 1.3762 Epoch 243/1000 168/168 [==============================] - 0s 114us/step - loss: 1.3740 Epoch 244/1000 168/168 [==============================] - 0s 101us/step - loss: 1.3718 Epoch 245/1000 168/168 [==============================] - 0s 106us/step - loss: 1.3696 Epoch 246/1000 168/168 [==============================] - 0s 115us/step - loss: 1.3674 Epoch 247/1000 168/168 [==============================] - 0s 109us/step - loss: 1.3651 Epoch 248/1000 168/168 [==============================] - 0s 127us/step - loss: 1.3629 Epoch 249/1000 168/168 [==============================] - 0s 96us/step - loss: 1.3606 Epoch 250/1000 168/168 [==============================] - 0s 106us/step - loss: 1.3583 Epoch 251/1000 168/168 [==============================] - 0s 95us/step - loss: 1.3560 Epoch 252/1000 168/168 [==============================] - 0s 118us/step - loss: 1.3538 Epoch 253/1000 168/168 [==============================] - 0s 136us/step - loss: 1.3515 Epoch 254/1000 168/168 [==============================] - 0s 103us/step - loss: 1.3492 Epoch 255/1000 168/168 [==============================] - 0s 110us/step - loss: 1.3468 Epoch 256/1000 168/168 [==============================] - 0s 106us/step - loss: 1.3445 Epoch 257/1000 168/168 [==============================] - 0s 145us/step - loss: 1.3422 Epoch 258/1000 168/168 [==============================] - 0s 123us/step - loss: 1.3399 Epoch 259/1000 168/168 [==============================] - 0s 110us/step - loss: 1.3375 Epoch 260/1000 168/168 [==============================] - 0s 106us/step - loss: 1.3352 Epoch 261/1000 168/168 [==============================] - 0s 116us/step - loss: 1.3328 Epoch 262/1000 168/168 [==============================] - 0s 106us/step - loss: 1.3305 Epoch 263/1000 168/168 [==============================] - 0s 101us/step - loss: 1.3281 Epoch 264/1000 168/168 [==============================] - 0s 108us/step - loss: 1.3257 Epoch 265/1000 168/168 [==============================] - 0s 121us/step - loss: 1.3233 Epoch 266/1000 168/168 [==============================] - 0s 104us/step - loss: 1.3210 Epoch 267/1000 168/168 [==============================] - 0s 101us/step - loss: 1.3186 Epoch 268/1000 168/168 [==============================] - 0s 111us/step - loss: 1.3162 Epoch 269/1000 168/168 [==============================] - 0s 99us/step - loss: 1.3138 Epoch 270/1000 168/168 [==============================] - 0s 113us/step - loss: 1.3114 Epoch 271/1000 168/168 [==============================] - 0s 98us/step - loss: 1.3090 Epoch 272/1000 168/168 [==============================] - 0s 112us/step - loss: 1.3066 Epoch 273/1000 168/168 [==============================] - 0s 104us/step - loss: 1.3042 Epoch 274/1000 168/168 [==============================] - 0s 106us/step - loss: 1.3018 Epoch 275/1000 168/168 [==============================] - 0s 127us/step - loss: 1.2994 Epoch 276/1000 168/168 [==============================] - 0s 102us/step - loss: 1.2970 Epoch 277/1000 168/168 [==============================] - 0s 104us/step - loss: 1.2946 Epoch 278/1000 168/168 [==============================] - 0s 103us/step - loss: 1.2922 Epoch 279/1000 168/168 [==============================] - 0s 116us/step - loss: 1.2898 Epoch 280/1000 168/168 [==============================] - 0s 116us/step - loss: 1.2874 Epoch 281/1000 168/168 [==============================] - 0s 100us/step - loss: 1.2849 Epoch 282/1000 168/168 [==============================] - 0s 123us/step - loss: 1.2825 Epoch 283/1000 168/168 [==============================] - 0s 104us/step - loss: 1.2801 Epoch 284/1000 168/168 [==============================] - 0s 126us/step - loss: 1.2777 Epoch 285/1000 168/168 [==============================] - 0s 113us/step - loss: 1.2753 Epoch 286/1000 168/168 [==============================] - 0s 112us/step - loss: 1.2729 Epoch 287/1000 168/168 [==============================] - 0s 115us/step - loss: 1.2705 Epoch 288/1000 168/168 [==============================] - 0s 103us/step - loss: 1.2681 Epoch 289/1000 168/168 [==============================] - 0s 105us/step - loss: 1.2657 Epoch 290/1000 168/168 [==============================] - 0s 101us/step - loss: 1.2633 Epoch 291/1000 168/168 [==============================] - 0s 120us/step - loss: 1.2609 Epoch 292/1000 168/168 [==============================] - 0s 117us/step - loss: 1.2585 Epoch 293/1000 168/168 [==============================] - 0s 103us/step - loss: 1.2561 Epoch 294/1000 168/168 [==============================] - 0s 111us/step - loss: 1.2537 Epoch 295/1000 168/168 [==============================] - 0s 98us/step - loss: 1.2513 Epoch 296/1000 168/168 [==============================] - 0s 110us/step - loss: 1.2489 Epoch 297/1000 168/168 [==============================] - 0s 94us/step - loss: 1.2466 Epoch 298/1000 168/168 [==============================] - 0s 98us/step - loss: 1.2442 Epoch 299/1000 168/168 [==============================] - 0s 106us/step - loss: 1.2418 Epoch 300/1000 168/168 [==============================] - 0s 135us/step - loss: 1.2394 Epoch 301/1000 168/168 [==============================] - 0s 118us/step - loss: 1.2371 Epoch 302/1000 168/168 [==============================] - 0s 105us/step - loss: 1.2347 Epoch 303/1000 168/168 [==============================] - 0s 105us/step - loss: 1.2323 Epoch 304/1000 168/168 [==============================] - 0s 103us/step - loss: 1.2300 Epoch 305/1000 168/168 [==============================] - 0s 113us/step - loss: 1.2276 Epoch 306/1000 168/168 [==============================] - 0s 119us/step - loss: 1.2253 Epoch 307/1000 168/168 [==============================] - 0s 118us/step - loss: 1.2229 Epoch 308/1000 168/168 [==============================] - 0s 101us/step - loss: 1.2206 Epoch 309/1000 168/168 [==============================] - 0s 114us/step - loss: 1.2183 Epoch 310/1000 168/168 [==============================] - 0s 106us/step - loss: 1.2159 Epoch 311/1000 168/168 [==============================] - 0s 114us/step - loss: 1.2136 Epoch 312/1000 168/168 [==============================] - 0s 133us/step - loss: 1.2113 Epoch 313/1000 168/168 [==============================] - 0s 114us/step - loss: 1.2090 Epoch 314/1000 168/168 [==============================] - 0s 103us/step - loss: 1.2066 Epoch 315/1000 168/168 [==============================] - 0s 110us/step - loss: 1.2043 Epoch 316/1000 168/168 [==============================] - 0s 103us/step - loss: 1.2020 Epoch 317/1000 168/168 [==============================] - 0s 106us/step - loss: 1.1997 Epoch 318/1000 168/168 [==============================] - 0s 109us/step - loss: 1.1974 Epoch 319/1000 168/168 [==============================] - 0s 99us/step - loss: 1.1951 Epoch 320/1000 168/168 [==============================] - 0s 99us/step - loss: 1.1929 Epoch 321/1000 168/168 [==============================] - 0s 135us/step - loss: 1.1906 Epoch 322/1000 168/168 [==============================] - 0s 122us/step - loss: 1.1883 Epoch 323/1000 168/168 [==============================] - 0s 118us/step - loss: 1.1860 Epoch 324/1000 168/168 [==============================] - 0s 98us/step - loss: 1.1838 Epoch 325/1000 168/168 [==============================] - 0s 97us/step - loss: 1.1815 Epoch 326/1000 168/168 [==============================] - 0s 103us/step - loss: 1.1792 Epoch 327/1000 168/168 [==============================] - 0s 108us/step - loss: 1.1770 Epoch 328/1000 168/168 [==============================] - 0s 127us/step - loss: 1.1748 Epoch 329/1000 168/168 [==============================] - 0s 113us/step - loss: 1.1725 Epoch 330/1000 168/168 [==============================] - 0s 111us/step - loss: 1.1703 Epoch 331/1000 168/168 [==============================] - 0s 103us/step - loss: 1.1680 Epoch 332/1000 168/168 [==============================] - 0s 121us/step - loss: 1.1658 Epoch 333/1000 168/168 [==============================] - 0s 98us/step - loss: 1.1636 Epoch 334/1000 168/168 [==============================] - 0s 105us/step - loss: 1.1614 Epoch 335/1000 168/168 [==============================] - 0s 104us/step - loss: 1.1592 Epoch 336/1000 168/168 [==============================] - 0s 104us/step - loss: 1.1570 Epoch 337/1000 168/168 [==============================] - 0s 104us/step - loss: 1.1548 Epoch 338/1000 168/168 [==============================] - 0s 100us/step - loss: 1.1526 Epoch 339/1000 168/168 [==============================] - 0s 102us/step - loss: 1.1504 Epoch 340/1000 168/168 [==============================] - 0s 97us/step - loss: 1.1482 Epoch 341/1000 168/168 [==============================] - 0s 120us/step - loss: 1.1460 Epoch 342/1000 168/168 [==============================] - 0s 103us/step - loss: 1.1439 Epoch 343/1000 168/168 [==============================] - 0s 102us/step - loss: 1.1417 Epoch 344/1000 168/168 [==============================] - 0s 112us/step - loss: 1.1395 Epoch 345/1000 168/168 [==============================] - 0s 122us/step - loss: 1.1374 Epoch 346/1000 168/168 [==============================] - 0s 126us/step - loss: 1.1352 Epoch 347/1000 168/168 [==============================] - 0s 107us/step - loss: 1.1331 Epoch 348/1000 168/168 [==============================] - 0s 102us/step - loss: 1.1309 Epoch 349/1000 168/168 [==============================] - 0s 103us/step - loss: 1.1288 Epoch 350/1000 168/168 [==============================] - 0s 110us/step - loss: 1.1267 Epoch 351/1000 168/168 [==============================] - 0s 105us/step - loss: 1.1245 Epoch 352/1000 168/168 [==============================] - 0s 107us/step - loss: 1.1224 Epoch 353/1000 168/168 [==============================] - 0s 123us/step - loss: 1.1203 Epoch 354/1000 168/168 [==============================] - 0s 108us/step - loss: 1.1182 Epoch 355/1000 168/168 [==============================] - 0s 106us/step - loss: 1.1161 Epoch 356/1000 168/168 [==============================] - 0s 109us/step - loss: 1.1140 Epoch 357/1000 168/168 [==============================] - 0s 100us/step - loss: 1.1119 Epoch 358/1000 168/168 [==============================] - 0s 102us/step - loss: 1.1098 Epoch 359/1000 168/168 [==============================] - 0s 105us/step - loss: 1.1077 Epoch 360/1000 168/168 [==============================] - 0s 96us/step - loss: 1.1057 Epoch 361/1000 168/168 [==============================] - 0s 104us/step - loss: 1.1036 Epoch 362/1000 168/168 [==============================] - 0s 124us/step - loss: 1.1015 Epoch 363/1000 168/168 [==============================] - 0s 102us/step - loss: 1.0995 Epoch 364/1000 168/168 [==============================] - 0s 171us/step - loss: 1.0974 Epoch 365/1000 168/168 [==============================] - 0s 106us/step - loss: 1.0954 Epoch 366/1000 168/168 [==============================] - 0s 116us/step - loss: 1.0933 Epoch 367/1000 168/168 [==============================] - 0s 128us/step - loss: 1.0913 Epoch 368/1000 168/168 [==============================] - 0s 107us/step - loss: 1.0893 Epoch 369/1000 168/168 [==============================] - 0s 119us/step - loss: 1.0872 Epoch 370/1000 168/168 [==============================] - 0s 115us/step - loss: 1.0852 Epoch 371/1000 168/168 [==============================] - 0s 128us/step - loss: 1.0832 Epoch 372/1000 168/168 [==============================] - 0s 101us/step - loss: 1.0812 Epoch 373/1000 168/168 [==============================] - 0s 107us/step - loss: 1.0792 Epoch 374/1000 168/168 [==============================] - 0s 120us/step - loss: 1.0772 Epoch 375/1000 168/168 [==============================] - 0s 124us/step - loss: 1.0752 Epoch 376/1000 168/168 [==============================] - 0s 99us/step - loss: 1.0732 Epoch 377/1000 168/168 [==============================] - 0s 102us/step - loss: 1.0712 Epoch 378/1000 168/168 [==============================] - 0s 108us/step - loss: 1.0692 Epoch 379/1000 168/168 [==============================] - 0s 134us/step - loss: 1.0672 Epoch 380/1000 168/168 [==============================] - 0s 137us/step - loss: 1.0653 Epoch 381/1000 168/168 [==============================] - 0s 105us/step - loss: 1.0633 Epoch 382/1000 168/168 [==============================] - 0s 106us/step - loss: 1.0613 Epoch 383/1000 168/168 [==============================] - 0s 106us/step - loss: 1.0594 Epoch 384/1000 168/168 [==============================] - 0s 100us/step - loss: 1.0574 Epoch 385/1000 168/168 [==============================] - 0s 110us/step - loss: 1.0555 Epoch 386/1000 168/168 [==============================] - 0s 105us/step - loss: 1.0536 Epoch 387/1000 168/168 [==============================] - 0s 102us/step - loss: 1.0516 Epoch 388/1000 168/168 [==============================] - 0s 113us/step - loss: 1.0497 Epoch 389/1000 168/168 [==============================] - 0s 109us/step - loss: 1.0478 Epoch 390/1000 168/168 [==============================] - 0s 105us/step - loss: 1.0458 Epoch 391/1000 168/168 [==============================] - 0s 121us/step - loss: 1.0439 Epoch 392/1000 168/168 [==============================] - 0s 108us/step - loss: 1.0420 Epoch 393/1000 168/168 [==============================] - 0s 113us/step - loss: 1.0401 Epoch 394/1000 168/168 [==============================] - 0s 100us/step - loss: 1.0382 Epoch 395/1000 168/168 [==============================] - 0s 101us/step - loss: 1.0363 Epoch 396/1000 168/168 [==============================] - 0s 111us/step - loss: 1.0344 Epoch 397/1000 168/168 [==============================] - 0s 135us/step - loss: 1.0325 Epoch 398/1000 168/168 [==============================] - 0s 101us/step - loss: 1.0307 Epoch 399/1000 168/168 [==============================] - 0s 109us/step - loss: 1.0288 Epoch 400/1000 168/168 [==============================] - 0s 105us/step - loss: 1.0269 Epoch 401/1000 168/168 [==============================] - 0s 100us/step - loss: 1.0250 Epoch 402/1000 168/168 [==============================] - 0s 107us/step - loss: 1.0232 Epoch 403/1000 168/168 [==============================] - 0s 126us/step - loss: 1.0213 Epoch 404/1000 168/168 [==============================] - 0s 99us/step - loss: 1.0195 Epoch 405/1000 168/168 [==============================] - 0s 115us/step - loss: 1.0176 Epoch 406/1000 168/168 [==============================] - 0s 118us/step - loss: 1.0158 Epoch 407/1000 168/168 [==============================] - 0s 101us/step - loss: 1.0139 Epoch 408/1000 168/168 [==============================] - 0s 107us/step - loss: 1.0121 Epoch 409/1000 168/168 [==============================] - 0s 97us/step - loss: 1.0103 Epoch 410/1000 168/168 [==============================] - 0s 109us/step - loss: 1.0084 Epoch 411/1000 168/168 [==============================] - 0s 104us/step - loss: 1.0066 Epoch 412/1000 168/168 [==============================] - 0s 118us/step - loss: 1.0048 Epoch 413/1000 168/168 [==============================] - 0s 106us/step - loss: 1.0030 Epoch 414/1000 168/168 [==============================] - 0s 102us/step - loss: 1.0012 Epoch 415/1000 168/168 [==============================] - 0s 128us/step - loss: 0.9994 Epoch 416/1000 168/168 [==============================] - 0s 109us/step - loss: 0.9976 Epoch 417/1000 168/168 [==============================] - 0s 98us/step - loss: 0.9958 Epoch 418/1000 168/168 [==============================] - 0s 108us/step - loss: 0.9940 Epoch 419/1000 168/168 [==============================] - 0s 110us/step - loss: 0.9922 Epoch 420/1000 168/168 [==============================] - 0s 108us/step - loss: 0.9904 Epoch 421/1000 168/168 [==============================] - 0s 108us/step - loss: 0.9886 Epoch 422/1000 168/168 [==============================] - 0s 109us/step - loss: 0.9869 Epoch 423/1000 168/168 [==============================] - 0s 101us/step - loss: 0.9851 Epoch 424/1000 168/168 [==============================] - 0s 127us/step - loss: 0.9833 Epoch 425/1000 168/168 [==============================] - 0s 134us/step - loss: 0.9816 Epoch 426/1000 168/168 [==============================] - 0s 124us/step - loss: 0.9798 Epoch 427/1000 168/168 [==============================] - 0s 113us/step - loss: 0.9781 Epoch 428/1000 168/168 [==============================] - 0s 117us/step - loss: 0.9763 Epoch 429/1000 168/168 [==============================] - 0s 105us/step - loss: 0.9746 Epoch 430/1000 168/168 [==============================] - 0s 119us/step - loss: 0.9729 Epoch 431/1000 168/168 [==============================] - 0s 100us/step - loss: 0.9712 Epoch 432/1000 168/168 [==============================] - 0s 133us/step - loss: 0.9694 Epoch 433/1000 168/168 [==============================] - 0s 107us/step - loss: 0.9677 Epoch 434/1000 168/168 [==============================] - 0s 106us/step - loss: 0.9660 Epoch 435/1000 168/168 [==============================] - 0s 101us/step - loss: 0.9643 Epoch 436/1000 168/168 [==============================] - 0s 126us/step - loss: 0.9626 Epoch 437/1000 168/168 [==============================] - 0s 103us/step - loss: 0.9609 Epoch 438/1000 168/168 [==============================] - 0s 107us/step - loss: 0.9592 Epoch 439/1000 168/168 [==============================] - 0s 107us/step - loss: 0.9575 Epoch 440/1000 168/168 [==============================] - 0s 109us/step - loss: 0.9559 Epoch 441/1000 168/168 [==============================] - 0s 108us/step - loss: 0.9542 Epoch 442/1000 168/168 [==============================] - 0s 108us/step - loss: 0.9525 Epoch 443/1000 168/168 [==============================] - 0s 102us/step - loss: 0.9509 Epoch 444/1000 168/168 [==============================] - 0s 103us/step - loss: 0.9492 Epoch 445/1000 168/168 [==============================] - 0s 99us/step - loss: 0.9476 Epoch 446/1000 168/168 [==============================] - 0s 102us/step - loss: 0.9460 Epoch 447/1000 168/168 [==============================] - 0s 105us/step - loss: 0.9443 Epoch 448/1000 168/168 [==============================] - 0s 119us/step - loss: 0.9427 Epoch 449/1000 168/168 [==============================] - 0s 107us/step - loss: 0.9411 Epoch 450/1000 168/168 [==============================] - 0s 114us/step - loss: 0.9395 Epoch 451/1000 168/168 [==============================] - 0s 107us/step - loss: 0.9379 Epoch 452/1000 168/168 [==============================] - 0s 108us/step - loss: 0.9363 Epoch 453/1000 168/168 [==============================] - 0s 122us/step - loss: 0.9347 Epoch 454/1000 168/168 [==============================] - 0s 111us/step - loss: 0.9331 Epoch 455/1000 168/168 [==============================] - 0s 113us/step - loss: 0.9316 Epoch 456/1000 168/168 [==============================] - 0s 108us/step - loss: 0.9300 Epoch 457/1000 168/168 [==============================] - 0s 107us/step - loss: 0.9284 Epoch 458/1000 168/168 [==============================] - 0s 140us/step - loss: 0.9269 Epoch 459/1000 168/168 [==============================] - 0s 112us/step - loss: 0.9254 Epoch 460/1000 168/168 [==============================] - 0s 106us/step - loss: 0.9238 Epoch 461/1000 168/168 [==============================] - 0s 123us/step - loss: 0.9223 Epoch 462/1000 168/168 [==============================] - 0s 116us/step - loss: 0.9208 Epoch 463/1000 168/168 [==============================] - 0s 101us/step - loss: 0.9193 Epoch 464/1000 168/168 [==============================] - 0s 110us/step - loss: 0.9178 Epoch 465/1000 168/168 [==============================] - 0s 115us/step - loss: 0.9163 Epoch 466/1000 168/168 [==============================] - 0s 103us/step - loss: 0.9148 Epoch 467/1000 168/168 [==============================] - 0s 112us/step - loss: 0.9134 Epoch 468/1000 168/168 [==============================] - 0s 112us/step - loss: 0.9119 Epoch 469/1000 168/168 [==============================] - 0s 102us/step - loss: 0.9105 Epoch 470/1000 168/168 [==============================] - 0s 95us/step - loss: 0.9090 Epoch 471/1000 168/168 [==============================] - 0s 101us/step - loss: 0.9076 Epoch 472/1000 168/168 [==============================] - 0s 104us/step - loss: 0.9062 Epoch 473/1000 168/168 [==============================] - 0s 121us/step - loss: 0.9047 Epoch 474/1000 168/168 [==============================] - 0s 102us/step - loss: 0.9033 Epoch 475/1000 168/168 [==============================] - 0s 109us/step - loss: 0.9019 Epoch 476/1000 168/168 [==============================] - 0s 104us/step - loss: 0.9005 Epoch 477/1000 168/168 [==============================] - 0s 105us/step - loss: 0.8991 Epoch 478/1000 168/168 [==============================] - 0s 99us/step - loss: 0.8978 Epoch 479/1000 168/168 [==============================] - 0s 131us/step - loss: 0.8964 Epoch 480/1000 168/168 [==============================] - 0s 101us/step - loss: 0.8951 Epoch 481/1000 168/168 [==============================] - 0s 115us/step - loss: 0.8937 Epoch 482/1000 168/168 [==============================] - 0s 115us/step - loss: 0.8924 Epoch 483/1000 168/168 [==============================] - 0s 116us/step - loss: 0.8910 Epoch 484/1000 168/168 [==============================] - 0s 112us/step - loss: 0.8897 Epoch 485/1000 168/168 [==============================] - 0s 115us/step - loss: 0.8884 Epoch 486/1000 168/168 [==============================] - 0s 98us/step - loss: 0.8871 Epoch 487/1000 168/168 [==============================] - 0s 106us/step - loss: 0.8857 Epoch 488/1000 168/168 [==============================] - 0s 106us/step - loss: 0.8846 Epoch 489/1000 168/168 [==============================] - 0s 109us/step - loss: 0.8831 Epoch 490/1000 168/168 [==============================] - 0s 124us/step - loss: 0.8821 Epoch 491/1000 168/168 [==============================] - 0s 112us/step - loss: 0.8805 Epoch 492/1000 168/168 [==============================] - 0s 113us/step - loss: 0.8796 Epoch 493/1000 168/168 [==============================] - 0s 122us/step - loss: 0.8779 Epoch 494/1000 168/168 [==============================] - 0s 106us/step - loss: 0.8772 Epoch 495/1000 168/168 [==============================] - 0s 114us/step - loss: 0.8753 Epoch 496/1000 168/168 [==============================] - 0s 118us/step - loss: 0.8750 Epoch 497/1000 168/168 [==============================] - 0s 97us/step - loss: 0.8726 Epoch 498/1000 168/168 [==============================] - 0s 96us/step - loss: 0.8730 Epoch 499/1000 168/168 [==============================] - 0s 95us/step - loss: 0.8697 Epoch 500/1000 168/168 [==============================] - 0s 96us/step - loss: 0.8712 Epoch 501/1000 168/168 [==============================] - 0s 115us/step - loss: 0.8665 Epoch 502/1000 168/168 [==============================] - 0s 120us/step - loss: 0.8701 Epoch 503/1000 168/168 [==============================] - 0s 97us/step - loss: 0.8628 Epoch 504/1000 168/168 [==============================] - 0s 101us/step - loss: 0.8702 Epoch 505/1000 168/168 [==============================] - 0s 132us/step - loss: 0.8582 Epoch 506/1000 168/168 [==============================] - 0s 115us/step - loss: 0.8729 Epoch 507/1000 168/168 [==============================] - 0s 132us/step - loss: 0.8527 Epoch 508/1000 168/168 [==============================] - 0s 106us/step - loss: 0.8812 Epoch 509/1000 168/168 [==============================] - 0s 113us/step - loss: 0.8479 Epoch 510/1000 168/168 [==============================] - 0s 116us/step - loss: 0.9031 Epoch 511/1000 168/168 [==============================] - 0s 102us/step - loss: 0.8522 Epoch 512/1000 168/168 [==============================] - 0s 99us/step - loss: 0.9544 Epoch 513/1000 168/168 [==============================] - 0s 129us/step - loss: 0.8931 Epoch 514/1000 168/168 [==============================] - 0s 107us/step - loss: 1.0253 Epoch 515/1000 168/168 [==============================] - 0s 109us/step - loss: 0.9510 Epoch 516/1000 168/168 [==============================] - 0s 104us/step - loss: 0.9974 Epoch 517/1000 168/168 [==============================] - 0s 114us/step - loss: 0.9027 Epoch 518/1000 168/168 [==============================] - 0s 99us/step - loss: 0.9064 Epoch 519/1000 168/168 [==============================] - 0s 104us/step - loss: 0.8548 Epoch 520/1000 168/168 [==============================] - 0s 103us/step - loss: 0.8656 Epoch 521/1000 168/168 [==============================] - 0s 122us/step - loss: 0.8412 Epoch 522/1000 168/168 [==============================] - 0s 97us/step - loss: 0.8553 Epoch 523/1000 168/168 [==============================] - 0s 115us/step - loss: 0.8383 Epoch 524/1000 168/168 [==============================] - 0s 110us/step - loss: 0.8514 Epoch 525/1000 168/168 [==============================] - 0s 119us/step - loss: 0.8364 Epoch 526/1000 168/168 [==============================] - 0s 101us/step - loss: 0.8490 Epoch 527/1000 168/168 [==============================] - 0s 102us/step - loss: 0.8341 Epoch 528/1000 168/168 [==============================] - 0s 98us/step - loss: 0.8476 Epoch 529/1000 168/168 [==============================] - 0s 106us/step - loss: 0.8316 Epoch 530/1000 168/168 [==============================] - 0s 120us/step - loss: 0.8477 Epoch 531/1000 168/168 [==============================] - 0s 120us/step - loss: 0.8292 Epoch 532/1000 168/168 [==============================] - 0s 112us/step - loss: 0.8493 Epoch 533/1000 168/168 [==============================] - 0s 105us/step - loss: 0.8270 Epoch 534/1000 168/168 [==============================] - 0s 120us/step - loss: 0.8532 Epoch 535/1000 168/168 [==============================] - 0s 105us/step - loss: 0.8255 Epoch 536/1000 168/168 [==============================] - 0s 98us/step - loss: 0.8609 Epoch 537/1000 168/168 [==============================] - 0s 115us/step - loss: 0.8264 Epoch 538/1000 168/168 [==============================] - 0s 127us/step - loss: 0.8744 Epoch 539/1000 168/168 [==============================] - 0s 125us/step - loss: 0.8325 Epoch 540/1000 168/168 [==============================] - 0s 109us/step - loss: 0.8943 Epoch 541/1000 168/168 [==============================] - 0s 125us/step - loss: 0.8463 Epoch 542/1000 168/168 [==============================] - 0s 105us/step - loss: 0.9134 Epoch 543/1000 168/168 [==============================] - 0s 118us/step - loss: 0.8606 Epoch 544/1000 168/168 [==============================] - 0s 109us/step - loss: 0.9133 Epoch 545/1000 168/168 [==============================] - 0s 101us/step - loss: 0.8569 Epoch 546/1000 168/168 [==============================] - 0s 103us/step - loss: 0.8897 Epoch 547/1000 168/168 [==============================] - 0s 112us/step - loss: 0.8390 Epoch 548/1000 168/168 [==============================] - 0s 110us/step - loss: 0.8630 Epoch 549/1000 168/168 [==============================] - 0s 104us/step - loss: 0.8245 Epoch 550/1000 168/168 [==============================] - 0s 98us/step - loss: 0.8454 Epoch 551/1000 168/168 [==============================] - 0s 105us/step - loss: 0.8169 Epoch 552/1000 168/168 [==============================] - 0s 120us/step - loss: 0.8356 Epoch 553/1000 168/168 [==============================] - 0s 102us/step - loss: 0.8130 Epoch 554/1000 168/168 [==============================] - 0s 100us/step - loss: 0.8302 Epoch 555/1000 168/168 [==============================] - 0s 112us/step - loss: 0.8105 Epoch 556/1000 168/168 [==============================] - 0s 99us/step - loss: 0.8273 Epoch 557/1000 168/168 [==============================] - 0s 129us/step - loss: 0.8084 Epoch 558/1000 168/168 [==============================] - 0s 135us/step - loss: 0.8261 Epoch 559/1000 168/168 [==============================] - 0s 102us/step - loss: 0.8066 Epoch 560/1000 168/168 [==============================] - 0s 107us/step - loss: 0.8265 Epoch 561/1000 168/168 [==============================] - 0s 103us/step - loss: 0.8052 Epoch 562/1000 168/168 [==============================] - 0s 108us/step - loss: 0.8287 Epoch 563/1000 168/168 [==============================] - 0s 111us/step - loss: 0.8044 Epoch 564/1000 168/168 [==============================] - 0s 120us/step - loss: 0.8332 Epoch 565/1000 168/168 [==============================] - 0s 108us/step - loss: 0.8051 Epoch 566/1000 168/168 [==============================] - 0s 123us/step - loss: 0.8411 Epoch 567/1000 168/168 [==============================] - 0s 147us/step - loss: 0.8086 Epoch 568/1000 168/168 [==============================] - 0s 103us/step - loss: 0.8525 Epoch 569/1000 168/168 [==============================] - 0s 102us/step - loss: 0.8163 Epoch 570/1000 168/168 [==============================] - 0s 97us/step - loss: 0.8654 Epoch 571/1000 168/168 [==============================] - 0s 115us/step - loss: 0.8265 Epoch 572/1000 168/168 [==============================] - 0s 106us/step - loss: 0.8723 Epoch 573/1000 168/168 [==============================] - 0s 105us/step - loss: 0.8312 Epoch 574/1000 168/168 [==============================] - 0s 125us/step - loss: 0.8654 Epoch 575/1000 168/168 [==============================] - 0s 129us/step - loss: 0.8240 Epoch 576/1000 168/168 [==============================] - 0s 100us/step - loss: 0.8481 Epoch 577/1000 168/168 [==============================] - 0s 104us/step - loss: 0.8114 Epoch 578/1000 168/168 [==============================] - 0s 108us/step - loss: 0.8309 Epoch 579/1000 168/168 [==============================] - 0s 124us/step - loss: 0.8014 Epoch 580/1000 168/168 [==============================] - 0s 117us/step - loss: 0.8189 Epoch 581/1000 168/168 [==============================] - 0s 101us/step - loss: 0.7954 Epoch 582/1000 168/168 [==============================] - 0s 107us/step - loss: 0.8119 Epoch 583/1000 168/168 [==============================] - 0s 117us/step - loss: 0.7920 Epoch 584/1000 168/168 [==============================] - 0s 107us/step - loss: 0.8082 Epoch 585/1000 168/168 [==============================] - 0s 119us/step - loss: 0.7897 Epoch 586/1000 168/168 [==============================] - 0s 110us/step - loss: 0.8066 Epoch 587/1000 168/168 [==============================] - 0s 106us/step - loss: 0.7881 Epoch 588/1000 168/168 [==============================] - 0s 121us/step - loss: 0.8068 Epoch 589/1000 168/168 [==============================] - 0s 128us/step - loss: 0.7871 Epoch 590/1000 168/168 [==============================] - 0s 118us/step - loss: 0.8089 Epoch 591/1000 168/168 [==============================] - 0s 129us/step - loss: 0.7870 Epoch 592/1000 168/168 [==============================] - 0s 118us/step - loss: 0.8131 Epoch 593/1000 168/168 [==============================] - 0s 106us/step - loss: 0.7886 Epoch 594/1000 168/168 [==============================] - 0s 140us/step - loss: 0.8201 Epoch 595/1000 168/168 [==============================] - 0s 121us/step - loss: 0.7928 Epoch 596/1000 168/168 [==============================] - 0s 107us/step - loss: 0.8298 Epoch 597/1000 168/168 [==============================] - 0s 95us/step - loss: 0.8005 Epoch 598/1000 168/168 [==============================] - 0s 136us/step - loss: 0.8396 Epoch 599/1000 168/168 [==============================] - 0s 110us/step - loss: 0.8090 Epoch 600/1000 168/168 [==============================] - 0s 123us/step - loss: 0.8435 Epoch 601/1000 168/168 [==============================] - 0s 105us/step - loss: 0.8117 Epoch 602/1000 168/168 [==============================] - 0s 103us/step - loss: 0.8366 Epoch 603/1000 168/168 [==============================] - 0s 122us/step - loss: 0.8046 Epoch 604/1000 168/168 [==============================] - 0s 115us/step - loss: 0.8221 Epoch 605/1000 168/168 [==============================] - 0s 106us/step - loss: 0.7933 Epoch 606/1000 168/168 [==============================] - 0s 106us/step - loss: 0.8079 Epoch 607/1000 168/168 [==============================] - 0s 107us/step - loss: 0.7842 Epoch 608/1000 168/168 [==============================] - 0s 148us/step - loss: 0.7982 Epoch 609/1000 168/168 [==============================] - 0s 113us/step - loss: 0.7787 Epoch 610/1000 168/168 [==============================] - 0s 111us/step - loss: 0.7927 Epoch 611/1000 168/168 [==============================] - 0s 101us/step - loss: 0.7756 Epoch 612/1000 168/168 [==============================] - 0s 99us/step - loss: 0.7902 Epoch 613/1000 168/168 [==============================] - 0s 107us/step - loss: 0.7737 Epoch 614/1000 168/168 [==============================] - 0s 108us/step - loss: 0.7899 Epoch 615/1000 168/168 [==============================] - 0s 108us/step - loss: 0.7727 Epoch 616/1000 168/168 [==============================] - 0s 130us/step - loss: 0.7914 Epoch 617/1000 168/168 [==============================] - 0s 116us/step - loss: 0.7728 Epoch 618/1000 168/168 [==============================] - 0s 106us/step - loss: 0.7952 Epoch 619/1000 168/168 [==============================] - 0s 116us/step - loss: 0.7746 Epoch 620/1000 168/168 [==============================] - 0s 105us/step - loss: 0.8016 Epoch 621/1000 168/168 [==============================] - 0s 125us/step - loss: 0.7792 Epoch 622/1000 168/168 [==============================] - 0s 115us/step - loss: 0.8104 Epoch 623/1000 168/168 [==============================] - 0s 108us/step - loss: 0.7871 Epoch 624/1000 168/168 [==============================] - 0s 99us/step - loss: 0.8195 Epoch 625/1000 168/168 [==============================] - 0s 108us/step - loss: 0.7959 Epoch 626/1000 168/168 [==============================] - 0s 135us/step - loss: 0.8234 Epoch 627/1000 168/168 [==============================] - 0s 104us/step - loss: 0.7988 Epoch 628/1000 168/168 [==============================] - 0s 105us/step - loss: 0.8172 Epoch 629/1000 168/168 [==============================] - 0s 125us/step - loss: 0.7918 Epoch 630/1000 168/168 [==============================] - 0s 119us/step - loss: 0.8036 Epoch 631/1000 168/168 [==============================] - 0s 120us/step - loss: 0.7802 Epoch 632/1000 168/168 [==============================] - 0s 123us/step - loss: 0.7902 Epoch 633/1000 168/168 [==============================] - 0s 121us/step - loss: 0.7708 Epoch 634/1000 168/168 [==============================] - 0s 118us/step - loss: 0.7812 Epoch 635/1000 168/168 [==============================] - 0s 105us/step - loss: 0.7652 Epoch 636/1000 168/168 [==============================] - 0s 128us/step - loss: 0.7766 Epoch 637/1000 168/168 [==============================] - 0s 122us/step - loss: 0.7622 Epoch 638/1000 168/168 [==============================] - 0s 104us/step - loss: 0.7749 Epoch 639/1000 168/168 [==============================] - 0s 147us/step - loss: 0.7607 Epoch 640/1000 168/168 [==============================] - 0s 112us/step - loss: 0.7755 Epoch 641/1000 168/168 [==============================] - 0s 107us/step - loss: 0.7604 Epoch 642/1000 168/168 [==============================] - 0s 116us/step - loss: 0.7784 Epoch 643/1000 168/168 [==============================] - 0s 125us/step - loss: 0.7617 Epoch 644/1000 168/168 [==============================] - 0s 102us/step - loss: 0.7839 Epoch 645/1000 168/168 [==============================] - 0s 104us/step - loss: 0.7659 Epoch 646/1000 168/168 [==============================] - 0s 148us/step - loss: 0.7924 Epoch 647/1000 168/168 [==============================] - 0s 107us/step - loss: 0.7740 Epoch 648/1000 168/168 [==============================] - 0s 118us/step - loss: 0.8026 Epoch 649/1000 168/168 [==============================] - 0s 100us/step - loss: 0.7849 Epoch 650/1000 168/168 [==============================] - 0s 105us/step - loss: 0.8096 Epoch 651/1000 168/168 [==============================] - 0s 113us/step - loss: 0.7917 Epoch 652/1000 168/168 [==============================] - 0s 123us/step - loss: 0.8064 Epoch 653/1000 168/168 [==============================] - 0s 121us/step - loss: 0.7866 Epoch 654/1000 168/168 [==============================] - 0s 101us/step - loss: 0.7930 Epoch 655/1000 168/168 [==============================] - 0s 113us/step - loss: 0.7731 Epoch 656/1000 168/168 [==============================] - 0s 105us/step - loss: 0.7778 Epoch 657/1000 168/168 [==============================] - 0s 149us/step - loss: 0.7611 Epoch 658/1000 168/168 [==============================] - 0s 101us/step - loss: 0.7674 Epoch 659/1000 168/168 [==============================] - 0s 119us/step - loss: 0.7542 Epoch 660/1000 168/168 [==============================] - 0s 96us/step - loss: 0.7623 Epoch 661/1000 168/168 [==============================] - 0s 136us/step - loss: 0.7508 Epoch 662/1000 168/168 [==============================] - 0s 102us/step - loss: 0.7606 Epoch 663/1000 168/168 [==============================] - 0s 127us/step - loss: 0.7492 Epoch 664/1000 168/168 [==============================] - 0s 106us/step - loss: 0.7614 Epoch 665/1000 168/168 [==============================] - 0s 100us/step - loss: 0.7490 Epoch 666/1000 168/168 [==============================] - 0s 120us/step - loss: 0.7647 Epoch 667/1000 168/168 [==============================] - 0s 104us/step - loss: 0.7509 Epoch 668/1000 168/168 [==============================] - 0s 103us/step - loss: 0.7713 Epoch 669/1000 168/168 [==============================] - 0s 102us/step - loss: 0.7566 Epoch 670/1000 168/168 [==============================] - 0s 104us/step - loss: 0.7818 Epoch 671/1000 168/168 [==============================] - 0s 121us/step - loss: 0.7681 Epoch 672/1000 168/168 [==============================] - 0s 98us/step - loss: 0.7949 Epoch 673/1000 168/168 [==============================] - 0s 99us/step - loss: 0.7836 Epoch 674/1000 168/168 [==============================] - 0s 162us/step - loss: 0.8030 Epoch 675/1000 168/168 [==============================] - 0s 136us/step - loss: 0.7913 Epoch 676/1000 168/168 [==============================] - 0s 117us/step - loss: 0.7962 Epoch 677/1000 168/168 [==============================] - 0s 100us/step - loss: 0.7802 Epoch 678/1000 168/168 [==============================] - 0s 103us/step - loss: 0.7771 Epoch 679/1000 168/168 [==============================] - 0s 102us/step - loss: 0.7606 Epoch 680/1000 168/168 [==============================] - 0s 132us/step - loss: 0.7596 Epoch 681/1000 168/168 [==============================] - 0s 103us/step - loss: 0.7474 Epoch 682/1000 168/168 [==============================] - 0s 116us/step - loss: 0.7502 Epoch 683/1000 168/168 [==============================] - 0s 100us/step - loss: 0.7414 Epoch 684/1000 168/168 [==============================] - 0s 119us/step - loss: 0.7465 Epoch 685/1000 168/168 [==============================] - 0s 105us/step - loss: 0.7387 Epoch 686/1000 168/168 [==============================] - 0s 101us/step - loss: 0.7458 Epoch 687/1000 168/168 [==============================] - 0s 100us/step - loss: 0.7374 Epoch 688/1000 168/168 [==============================] - 0s 101us/step - loss: 0.7472 Epoch 689/1000 168/168 [==============================] - 0s 92us/step - loss: 0.7374 Epoch 690/1000 168/168 [==============================] - 0s 94us/step - loss: 0.7513 Epoch 691/1000 168/168 [==============================] - 0s 132us/step - loss: 0.7399 Epoch 692/1000 168/168 [==============================] - 0s 101us/step - loss: 0.7598 Epoch 693/1000 168/168 [==============================] - 0s 110us/step - loss: 0.7482 Epoch 694/1000 168/168 [==============================] - 0s 120us/step - loss: 0.7749 Epoch 695/1000 168/168 [==============================] - 0s 92us/step - loss: 0.7673 Epoch 696/1000 168/168 [==============================] - 0s 108us/step - loss: 0.7947 Epoch 697/1000 168/168 [==============================] - 0s 103us/step - loss: 0.7937 Epoch 698/1000 168/168 [==============================] - 0s 106us/step - loss: 0.8052 Epoch 699/1000 168/168 [==============================] - 0s 122us/step - loss: 0.8012 Epoch 700/1000 168/168 [==============================] - 0s 105us/step - loss: 0.7886 Epoch 701/1000 168/168 [==============================] - 0s 105us/step - loss: 0.7738 Epoch 702/1000 168/168 [==============================] - 0s 102us/step - loss: 0.7578 Epoch 703/1000 168/168 [==============================] - 0s 101us/step - loss: 0.7448 Epoch 704/1000 168/168 [==============================] - 0s 100us/step - loss: 0.7389 Epoch 705/1000 168/168 [==============================] - 0s 101us/step - loss: 0.7332 Epoch 706/1000 168/168 [==============================] - 0s 106us/step - loss: 0.7326 Epoch 707/1000 168/168 [==============================] - 0s 107us/step - loss: 0.7297 Epoch 708/1000 168/168 [==============================] - 0s 124us/step - loss: 0.7306 Epoch 709/1000 168/168 [==============================] - 0s 127us/step - loss: 0.7280 Epoch 710/1000 168/168 [==============================] - 0s 117us/step - loss: 0.7298 Epoch 711/1000 168/168 [==============================] - 0s 100us/step - loss: 0.7267 Epoch 712/1000 168/168 [==============================] - 0s 106us/step - loss: 0.7294 Epoch 713/1000 168/168 [==============================] - 0s 128us/step - loss: 0.7253 Epoch 714/1000 168/168 [==============================] - 0s 104us/step - loss: 0.7299 Epoch 715/1000 168/168 [==============================] - 0s 106us/step - loss: 0.7242 Epoch 716/1000 168/168 [==============================] - 0s 107us/step - loss: 0.7320 Epoch 717/1000 168/168 [==============================] - 0s 130us/step - loss: 0.7242 Epoch 718/1000 168/168 [==============================] - 0s 103us/step - loss: 0.7382 Epoch 719/1000 168/168 [==============================] - 0s 97us/step - loss: 0.7288 Epoch 720/1000 168/168 [==============================] - 0s 98us/step - loss: 0.7541 Epoch 721/1000 168/168 [==============================] - 0s 98us/step - loss: 0.7490 Epoch 722/1000 168/168 [==============================] - 0s 107us/step - loss: 0.7877 Epoch 723/1000 168/168 [==============================] - 0s 101us/step - loss: 0.8035 Epoch 724/1000 168/168 [==============================] - 0s 104us/step - loss: 0.8297 Epoch 725/1000 168/168 [==============================] - 0s 118us/step - loss: 0.8564 Epoch 726/1000 168/168 [==============================] - 0s 99us/step - loss: 0.8222 Epoch 727/1000 168/168 [==============================] - 0s 102us/step - loss: 0.8111 Epoch 728/1000 168/168 [==============================] - 0s 111us/step - loss: 0.7535 Epoch 729/1000 168/168 [==============================] - 0s 110us/step - loss: 0.7392 Epoch 730/1000 168/168 [==============================] - 0s 102us/step - loss: 0.7225 Epoch 731/1000 168/168 [==============================] - 0s 110us/step - loss: 0.7268 Epoch 732/1000 168/168 [==============================] - 0s 96us/step - loss: 0.7209 Epoch 733/1000 168/168 [==============================] - 0s 149us/step - loss: 0.7251 Epoch 734/1000 168/168 [==============================] - 0s 103us/step - loss: 0.7199 Epoch 735/1000 168/168 [==============================] - 0s 101us/step - loss: 0.7242 Epoch 736/1000 168/168 [==============================] - 0s 98us/step - loss: 0.7185 Epoch 737/1000 168/168 [==============================] - 0s 99us/step - loss: 0.7245 Epoch 738/1000 168/168 [==============================] - 0s 100us/step - loss: 0.7179 Epoch 739/1000 168/168 [==============================] - 0s 119us/step - loss: 0.7265 Epoch 740/1000 168/168 [==============================] - 0s 102us/step - loss: 0.7190 Epoch 741/1000 168/168 [==============================] - 0s 107us/step - loss: 0.7317 Epoch 742/1000 168/168 [==============================] - 0s 111us/step - loss: 0.7240 Epoch 743/1000 168/168 [==============================] - 0s 117us/step - loss: 0.7425 Epoch 744/1000 168/168 [==============================] - 0s 123us/step - loss: 0.7380 Epoch 745/1000 168/168 [==============================] - 0s 110us/step - loss: 0.7607 Epoch 746/1000 168/168 [==============================] - 0s 108us/step - loss: 0.7650 Epoch 747/1000 168/168 [==============================] - 0s 119us/step - loss: 0.7805 Epoch 748/1000 168/168 [==============================] - 0s 102us/step - loss: 0.7908 Epoch 749/1000 168/168 [==============================] - 0s 107us/step - loss: 0.7817 Epoch 750/1000 168/168 [==============================] - 0s 108us/step - loss: 0.7811 Epoch 751/1000 168/168 [==============================] - 0s 114us/step - loss: 0.7542 Epoch 752/1000 168/168 [==============================] - 0s 113us/step - loss: 0.7428 Epoch 753/1000 168/168 [==============================] - 0s 107us/step - loss: 0.7260 Epoch 754/1000 168/168 [==============================] - 0s 101us/step - loss: 0.7193 Epoch 755/1000 168/168 [==============================] - 0s 106us/step - loss: 0.7145 Epoch 756/1000 168/168 [==============================] - 0s 117us/step - loss: 0.7122 Epoch 757/1000 168/168 [==============================] - 0s 104us/step - loss: 0.7113 Epoch 758/1000 168/168 [==============================] - 0s 110us/step - loss: 0.7099 Epoch 759/1000 168/168 [==============================] - 0s 124us/step - loss: 0.7100 Epoch 760/1000 168/168 [==============================] - 0s 112us/step - loss: 0.7085 Epoch 761/1000 168/168 [==============================] - 0s 123us/step - loss: 0.7092 Epoch 762/1000 168/168 [==============================] - 0s 114us/step - loss: 0.7071 Epoch 763/1000 168/168 [==============================] - 0s 117us/step - loss: 0.7086 Epoch 764/1000 168/168 [==============================] - 0s 100us/step - loss: 0.7057 Epoch 765/1000 168/168 [==============================] - 0s 116us/step - loss: 0.7089 Epoch 766/1000 168/168 [==============================] - 0s 95us/step - loss: 0.7046 Epoch 767/1000 168/168 [==============================] - 0s 109us/step - loss: 0.7112 Epoch 768/1000 168/168 [==============================] - 0s 126us/step - loss: 0.7051 Epoch 769/1000 168/168 [==============================] - 0s 104us/step - loss: 0.7194 Epoch 770/1000 168/168 [==============================] - 0s 114us/step - loss: 0.7139 Epoch 771/1000 168/168 [==============================] - 0s 113us/step - loss: 0.7437 Epoch 772/1000 168/168 [==============================] - 0s 138us/step - loss: 0.7548 Epoch 773/1000 168/168 [==============================] - 0s 108us/step - loss: 0.7976 Epoch 774/1000 168/168 [==============================] - 0s 120us/step - loss: 0.8496 Epoch 775/1000 168/168 [==============================] - 0s 111us/step - loss: 0.8448 Epoch 776/1000 168/168 [==============================] - 0s 114us/step - loss: 0.8692 Epoch 777/1000 168/168 [==============================] - 0s 104us/step - loss: 0.7819 Epoch 778/1000 168/168 [==============================] - 0s 103us/step - loss: 0.7584 Epoch 779/1000 168/168 [==============================] - 0s 127us/step - loss: 0.7115 Epoch 780/1000 168/168 [==============================] - 0s 109us/step - loss: 0.7148 Epoch 781/1000 168/168 [==============================] - 0s 108us/step - loss: 0.7065 Epoch 782/1000 168/168 [==============================] - 0s 109us/step - loss: 0.7108 Epoch 783/1000 168/168 [==============================] - 0s 130us/step - loss: 0.7056 Epoch 784/1000 168/168 [==============================] - 0s 104us/step - loss: 0.7082 Epoch 785/1000 168/168 [==============================] - 0s 115us/step - loss: 0.7029 Epoch 786/1000 168/168 [==============================] - 0s 129us/step - loss: 0.7073 Epoch 787/1000 168/168 [==============================] - 0s 102us/step - loss: 0.7017 Epoch 788/1000 168/168 [==============================] - 0s 123us/step - loss: 0.7085 Epoch 789/1000 168/168 [==============================] - 0s 126us/step - loss: 0.7027 Epoch 790/1000 168/168 [==============================] - 0s 107us/step - loss: 0.7127 Epoch 791/1000 168/168 [==============================] - 0s 112us/step - loss: 0.7076 Epoch 792/1000 168/168 [==============================] - 0s 118us/step - loss: 0.7221 Epoch 793/1000 168/168 [==============================] - 0s 101us/step - loss: 0.7211 Epoch 794/1000 168/168 [==============================] - 0s 106us/step - loss: 0.7382 Epoch 795/1000 168/168 [==============================] - 0s 108us/step - loss: 0.7459 Epoch 796/1000 168/168 [==============================] - 0s 128us/step - loss: 0.7560 Epoch 797/1000 168/168 [==============================] - 0s 111us/step - loss: 0.7693 Epoch 798/1000 168/168 [==============================] - 0s 114us/step - loss: 0.7585 Epoch 799/1000 168/168 [==============================] - 0s 118us/step - loss: 0.7629 Epoch 800/1000 168/168 [==============================] - 0s 135us/step - loss: 0.7368 Epoch 801/1000 168/168 [==============================] - 0s 110us/step - loss: 0.7295 Epoch 802/1000 168/168 [==============================] - 0s 105us/step - loss: 0.7115 Epoch 803/1000 168/168 [==============================] - 0s 114us/step - loss: 0.7048 Epoch 804/1000 168/168 [==============================] - 0s 101us/step - loss: 0.6991 Epoch 805/1000 168/168 [==============================] - 0s 109us/step - loss: 0.6956 Epoch 806/1000 168/168 [==============================] - 0s 123us/step - loss: 0.6954 Epoch 807/1000 168/168 [==============================] - 0s 107us/step - loss: 0.6924 Epoch 808/1000 168/168 [==============================] - 0s 113us/step - loss: 0.6945 Epoch 809/1000 168/168 [==============================] - 0s 117us/step - loss: 0.6909 Epoch 810/1000 168/168 [==============================] - 0s 106us/step - loss: 0.6955 Epoch 811/1000 168/168 [==============================] - 0s 144us/step - loss: 0.6908 Epoch 812/1000 168/168 [==============================] - 0s 147us/step - loss: 0.6998 Epoch 813/1000 168/168 [==============================] - 0s 118us/step - loss: 0.6953 Epoch 814/1000 168/168 [==============================] - 0s 163us/step - loss: 0.7125 Epoch 815/1000 168/168 [==============================] - 0s 125us/step - loss: 0.7152 Epoch 816/1000 168/168 [==============================] - 0s 149us/step - loss: 0.7424 Epoch 817/1000 168/168 [==============================] - 0s 117us/step - loss: 0.7682 Epoch 818/1000 168/168 [==============================] - 0s 103us/step - loss: 0.7840 Epoch 819/1000 168/168 [==============================] - 0s 130us/step - loss: 0.8212 Epoch 820/1000 168/168 [==============================] - 0s 126us/step - loss: 0.7833 Epoch 821/1000 168/168 [==============================] - 0s 111us/step - loss: 0.7859 Epoch 822/1000 168/168 [==============================] - 0s 105us/step - loss: 0.7253 Epoch 823/1000 168/168 [==============================] - 0s 103us/step - loss: 0.7121 Epoch 824/1000 168/168 [==============================] - 0s 132us/step - loss: 0.6916 Epoch 825/1000 168/168 [==============================] - 0s 100us/step - loss: 0.6923 Epoch 826/1000 168/168 [==============================] - 0s 118us/step - loss: 0.6873 Epoch 827/1000 168/168 [==============================] - 0s 109us/step - loss: 0.6897 Epoch 828/1000 168/168 [==============================] - 0s 107us/step - loss: 0.6862 Epoch 829/1000 168/168 [==============================] - 0s 119us/step - loss: 0.6898 Epoch 830/1000 168/168 [==============================] - 0s 105us/step - loss: 0.6856 Epoch 831/1000 168/168 [==============================] - 0s 133us/step - loss: 0.6917 Epoch 832/1000 168/168 [==============================] - 0s 105us/step - loss: 0.6872 Epoch 833/1000 168/168 [==============================] - 0s 126us/step - loss: 0.6975 Epoch 834/1000 168/168 [==============================] - 0s 156us/step - loss: 0.6952 Epoch 835/1000 168/168 [==============================] - 0s 109us/step - loss: 0.7115 Epoch 836/1000 168/168 [==============================] - 0s 106us/step - loss: 0.7182 Epoch 837/1000 168/168 [==============================] - 0s 105us/step - loss: 0.7362 Epoch 838/1000 168/168 [==============================] - 0s 122us/step - loss: 0.7577 Epoch 839/1000 168/168 [==============================] - 0s 105us/step - loss: 0.7582 Epoch 840/1000 168/168 [==============================] - 0s 106us/step - loss: 0.7784 Epoch 841/1000 168/168 [==============================] - 0s 126us/step - loss: 0.7463 Epoch 842/1000 168/168 [==============================] - 0s 106us/step - loss: 0.7454 Epoch 843/1000 168/168 [==============================] - 0s 117us/step - loss: 0.7094 Epoch 844/1000 168/168 [==============================] - 0s 111us/step - loss: 0.7003 Epoch 845/1000 168/168 [==============================] - 0s 105us/step - loss: 0.6860 Epoch 846/1000 168/168 [==============================] - 0s 113us/step - loss: 0.6833 Epoch 847/1000 168/168 [==============================] - 0s 111us/step - loss: 0.6797 Epoch 848/1000 168/168 [==============================] - 0s 105us/step - loss: 0.6792 Epoch 849/1000 168/168 [==============================] - 0s 108us/step - loss: 0.6779 Epoch 850/1000 168/168 [==============================] - 0s 99us/step - loss: 0.6777 Epoch 851/1000 168/168 [==============================] - 0s 104us/step - loss: 0.6765 Epoch 852/1000 168/168 [==============================] - 0s 97us/step - loss: 0.6766 Epoch 853/1000 168/168 [==============================] - 0s 101us/step - loss: 0.6751 Epoch 854/1000 168/168 [==============================] - 0s 117us/step - loss: 0.6758 Epoch 855/1000 168/168 [==============================] - 0s 144us/step - loss: 0.6737 Epoch 856/1000 168/168 [==============================] - 0s 106us/step - loss: 0.6758 Epoch 857/1000 168/168 [==============================] - 0s 102us/step - loss: 0.6728 Epoch 858/1000 168/168 [==============================] - 0s 107us/step - loss: 0.6780 Epoch 859/1000 168/168 [==============================] - 0s 98us/step - loss: 0.6743 Epoch 860/1000 168/168 [==============================] - 0s 98us/step - loss: 0.6873 Epoch 861/1000 168/168 [==============================] - 0s 99us/step - loss: 0.6885 Epoch 862/1000 168/168 [==============================] - 0s 104us/step - loss: 0.7195 Epoch 863/1000 168/168 [==============================] - 0s 133us/step - loss: 0.7537 Epoch 864/1000 168/168 [==============================] - 0s 107us/step - loss: 0.7917 Epoch 865/1000 168/168 [==============================] - 0s 104us/step - loss: 0.8575 Epoch 866/1000 168/168 [==============================] - 0s 102us/step - loss: 0.8162 Epoch 867/1000 168/168 [==============================] - 0s 110us/step - loss: 0.8148 Epoch 868/1000 168/168 [==============================] - 0s 102us/step - loss: 0.7199 Epoch 869/1000 168/168 [==============================] - 0s 108us/step - loss: 0.7018 Epoch 870/1000 168/168 [==============================] - 0s 115us/step - loss: 0.6769 Epoch 871/1000 168/168 [==============================] - 0s 145us/step - loss: 0.6802 Epoch 872/1000 168/168 [==============================] - 0s 104us/step - loss: 0.6756 Epoch 873/1000 168/168 [==============================] - 0s 105us/step - loss: 0.6778 Epoch 874/1000 168/168 [==============================] - 0s 113us/step - loss: 0.6741 Epoch 875/1000 168/168 [==============================] - 0s 99us/step - loss: 0.6779 Epoch 876/1000 168/168 [==============================] - 0s 130us/step - loss: 0.6737 Epoch 877/1000 168/168 [==============================] - 0s 108us/step - loss: 0.6799 Epoch 878/1000 168/168 [==============================] - 0s 100us/step - loss: 0.6768 Epoch 879/1000 168/168 [==============================] - 0s 138us/step - loss: 0.6862 Epoch 880/1000 168/168 [==============================] - 0s 102us/step - loss: 0.6868 Epoch 881/1000 168/168 [==============================] - 0s 105us/step - loss: 0.6987 Epoch 882/1000 168/168 [==============================] - 0s 94us/step - loss: 0.7071 Epoch 883/1000 168/168 [==============================] - 0s 125us/step - loss: 0.7161 Epoch 884/1000 168/168 [==============================] - 0s 106us/step - loss: 0.7318 Epoch 885/1000 168/168 [==============================] - 0s 116us/step - loss: 0.7269 Epoch 886/1000 168/168 [==============================] - 0s 99us/step - loss: 0.7383 Epoch 887/1000 168/168 [==============================] - 0s 117us/step - loss: 0.7160 Epoch 888/1000 168/168 [==============================] - 0s 110us/step - loss: 0.7151 Epoch 889/1000 168/168 [==============================] - 0s 101us/step - loss: 0.6923 Epoch 890/1000 168/168 [==============================] - 0s 96us/step - loss: 0.6857 Epoch 891/1000 168/168 [==============================] - 0s 101us/step - loss: 0.6747 Epoch 892/1000 168/168 [==============================] - 0s 123us/step - loss: 0.6704 Epoch 893/1000 168/168 [==============================] - 0s 102us/step - loss: 0.6679 Epoch 894/1000 168/168 [==============================] - 0s 121us/step - loss: 0.6651 Epoch 895/1000 168/168 [==============================] - 0s 101us/step - loss: 0.6663 Epoch 896/1000 168/168 [==============================] - 0s 99us/step - loss: 0.6634 Epoch 897/1000 168/168 [==============================] - 0s 102us/step - loss: 0.6674 Epoch 898/1000 168/168 [==============================] - 0s 112us/step - loss: 0.6645 Epoch 899/1000 168/168 [==============================] - 0s 100us/step - loss: 0.6726 Epoch 900/1000 168/168 [==============================] - 0s 122us/step - loss: 0.6724 Epoch 901/1000 168/168 [==============================] - 0s 113us/step - loss: 0.6874 Epoch 902/1000 168/168 [==============================] - 0s 108us/step - loss: 0.6990 Epoch 903/1000 168/168 [==============================] - 0s 100us/step - loss: 0.7191 Epoch 904/1000 168/168 [==============================] - 0s 124us/step - loss: 0.7515 Epoch 905/1000 168/168 [==============================] - 0s 102us/step - loss: 0.7522 Epoch 906/1000 168/168 [==============================] - 0s 98us/step - loss: 0.7773 Epoch 907/1000 168/168 [==============================] - 0s 98us/step - loss: 0.7342 Epoch 908/1000 168/168 [==============================] - 0s 100us/step - loss: 0.7303 Epoch 909/1000 168/168 [==============================] - 0s 127us/step - loss: 0.6870 Epoch 910/1000 168/168 [==============================] - 0s 93us/step - loss: 0.6762 Epoch 911/1000 168/168 [==============================] - 0s 128us/step - loss: 0.6619 Epoch 912/1000 168/168 [==============================] - 0s 119us/step - loss: 0.6616 Epoch 913/1000 168/168 [==============================] - 0s 103us/step - loss: 0.6584 Epoch 914/1000 168/168 [==============================] - 0s 119us/step - loss: 0.6598 Epoch 915/1000 168/168 [==============================] - 0s 105us/step - loss: 0.6575 Epoch 916/1000 168/168 [==============================] - 0s 103us/step - loss: 0.6599 Epoch 917/1000 168/168 [==============================] - 0s 119us/step - loss: 0.6571 Epoch 918/1000 168/168 [==============================] - 0s 123us/step - loss: 0.6615 Epoch 919/1000 168/168 [==============================] - 0s 116us/step - loss: 0.6591 Epoch 920/1000 168/168 [==============================] - 0s 112us/step - loss: 0.6670 Epoch 921/1000 168/168 [==============================] - 0s 99us/step - loss: 0.6680 Epoch 922/1000 168/168 [==============================] - 0s 100us/step - loss: 0.6811 Epoch 923/1000 168/168 [==============================] - 0s 129us/step - loss: 0.6932 Epoch 924/1000 168/168 [==============================] - 0s 109us/step - loss: 0.7095 Epoch 925/1000 168/168 [==============================] - 0s 98us/step - loss: 0.7382 Epoch 926/1000 168/168 [==============================] - 0s 109us/step - loss: 0.7381 Epoch 927/1000 168/168 [==============================] - 0s 106us/step - loss: 0.7601 Epoch 928/1000 168/168 [==============================] - 0s 113us/step - loss: 0.7234 Epoch 929/1000 168/168 [==============================] - 0s 103us/step - loss: 0.7202 Epoch 930/1000 168/168 [==============================] - 0s 102us/step - loss: 0.6814 Epoch 931/1000 168/168 [==============================] - 0s 144us/step - loss: 0.6706 Epoch 932/1000 168/168 [==============================] - 0s 104us/step - loss: 0.6560 Epoch 933/1000 168/168 [==============================] - 0s 121us/step - loss: 0.6550 Epoch 934/1000 168/168 [==============================] - 0s 105us/step - loss: 0.6519 Epoch 935/1000 168/168 [==============================] - 0s 127us/step - loss: 0.6529 Epoch 936/1000 168/168 [==============================] - 0s 108us/step - loss: 0.6509 Epoch 937/1000 168/168 [==============================] - 0s 106us/step - loss: 0.6527 Epoch 938/1000 168/168 [==============================] - 0s 107us/step - loss: 0.6503 Epoch 939/1000 168/168 [==============================] - 0s 120us/step - loss: 0.6536 Epoch 940/1000 168/168 [==============================] - 0s 116us/step - loss: 0.6513 Epoch 941/1000 168/168 [==============================] - 0s 115us/step - loss: 0.6574 Epoch 942/1000 168/168 [==============================] - 0s 115us/step - loss: 0.6572 Epoch 943/1000 168/168 [==============================] - 0s 100us/step - loss: 0.6678 Epoch 944/1000 168/168 [==============================] - 0s 106us/step - loss: 0.6758 Epoch 945/1000 168/168 [==============================] - 0s 109us/step - loss: 0.6919 Epoch 946/1000 168/168 [==============================] - 0s 96us/step - loss: 0.7173 Epoch 947/1000 168/168 [==============================] - 0s 101us/step - loss: 0.7280 Epoch 948/1000 168/168 [==============================] - 0s 110us/step - loss: 0.7576 Epoch 949/1000 168/168 [==============================] - 0s 97us/step - loss: 0.7314 Epoch 950/1000 168/168 [==============================] - 0s 94us/step - loss: 0.7343 Epoch 951/1000 168/168 [==============================] - 0s 184us/step - loss: 0.6886 Epoch 952/1000 168/168 [==============================] - 0s 145us/step - loss: 0.6755 Epoch 953/1000 168/168 [==============================] - 0s 110us/step - loss: 0.6534 Epoch 954/1000 168/168 [==============================] - 0s 107us/step - loss: 0.6506 Epoch 955/1000 168/168 [==============================] - 0s 100us/step - loss: 0.6463 Epoch 956/1000 168/168 [==============================] - 0s 100us/step - loss: 0.6477 Epoch 957/1000 168/168 [==============================] - 0s 99us/step - loss: 0.6458 Epoch 958/1000 168/168 [==============================] - 0s 101us/step - loss: 0.6476 Epoch 959/1000 168/168 [==============================] - 0s 99us/step - loss: 0.6453 Epoch 960/1000 168/168 [==============================] - 0s 98us/step - loss: 0.6482 Epoch 961/1000 168/168 [==============================] - 0s 95us/step - loss: 0.6462 Epoch 962/1000 168/168 [==============================] - 0s 111us/step - loss: 0.6510 Epoch 963/1000 168/168 [==============================] - 0s 100us/step - loss: 0.6508 Epoch 964/1000 168/168 [==============================] - 0s 126us/step - loss: 0.6583 Epoch 965/1000 168/168 [==============================] - 0s 102us/step - loss: 0.6633 Epoch 966/1000 168/168 [==============================] - 0s 123us/step - loss: 0.6740 Epoch 967/1000 168/168 [==============================] - 0s 118us/step - loss: 0.6899 Epoch 968/1000 168/168 [==============================] - 0s 111us/step - loss: 0.7004 Epoch 969/1000 168/168 [==============================] - 0s 115us/step - loss: 0.7257 Epoch 970/1000 168/168 [==============================] - 0s 99us/step - loss: 0.7173 Epoch 971/1000 168/168 [==============================] - 0s 118us/step - loss: 0.7294 Epoch 972/1000 168/168 [==============================] - 0s 104us/step - loss: 0.6952 Epoch 973/1000 168/168 [==============================] - 0s 99us/step - loss: 0.6871 Epoch 974/1000 168/168 [==============================] - 0s 105us/step - loss: 0.6583 Epoch 975/1000 168/168 [==============================] - 0s 103us/step - loss: 0.6497 Epoch 976/1000 168/168 [==============================] - 0s 108us/step - loss: 0.6406 Epoch 977/1000 168/168 [==============================] - 0s 113us/step - loss: 0.6410 Epoch 978/1000 168/168 [==============================] - 0s 151us/step - loss: 0.6392 Epoch 979/1000 168/168 [==============================] - 0s 107us/step - loss: 0.6403 Epoch 980/1000 168/168 [==============================] - 0s 120us/step - loss: 0.6384 Epoch 981/1000 168/168 [==============================] - 0s 126us/step - loss: 0.6401 Epoch 982/1000 168/168 [==============================] - 0s 110us/step - loss: 0.6381 Epoch 983/1000 168/168 [==============================] - 0s 105us/step - loss: 0.6410 Epoch 984/1000 168/168 [==============================] - 0s 106us/step - loss: 0.6395 Epoch 985/1000 168/168 [==============================] - 0s 105us/step - loss: 0.6443 Epoch 986/1000 168/168 [==============================] - 0s 127us/step - loss: 0.6449 Epoch 987/1000 168/168 [==============================] - 0s 105us/step - loss: 0.6528 Epoch 988/1000 168/168 [==============================] - 0s 111us/step - loss: 0.6598 Epoch 989/1000 168/168 [==============================] - 0s 106us/step - loss: 0.6717 Epoch 990/1000 168/168 [==============================] - 0s 113us/step - loss: 0.6921 Epoch 991/1000 168/168 [==============================] - 0s 112us/step - loss: 0.7038 Epoch 992/1000 168/168 [==============================] - 0s 104us/step - loss: 0.7321 Epoch 993/1000 168/168 [==============================] - 0s 102us/step - loss: 0.7173 Epoch 994/1000 168/168 [==============================] - 0s 111us/step - loss: 0.7236 Epoch 995/1000 168/168 [==============================] - 0s 105us/step - loss: 0.6829 Epoch 996/1000 168/168 [==============================] - 0s 113us/step - loss: 0.6693 Epoch 997/1000 168/168 [==============================] - 0s 105us/step - loss: 0.6434 Epoch 998/1000 168/168 [==============================] - 0s 128us/step - loss: 0.6387 Epoch 999/1000 168/168 [==============================] - 0s 118us/step - loss: 0.6343 Epoch 1000/1000 168/168 [==============================] - 0s 141us/step - loss: 0.6357
Out[69]:
<keras.callbacks.callbacks.History at 0x14c2c58c0b70>
We evaluate the model on all data
In [70]:
res = model.predict(x).squeeze()
Plotting the data, the model fits the data OK but performs a lot less well on the last 50 time steps which were not in the training set
In [71]:
plt.plot(time[15:], obs_data[15:] - nowcast_data[15:], label='Error') plt.plot(time[15:], res, label='Correction') plt.legend()
Out[71]:
<matplotlib.legend.Legend at 0x14c2c4ff0a58>
Just for illustration here are the data and the corrected data. The results are not great which is not a surprise but the workflow is there.
In [73]:
plt.plot(time, nowcast_data, label='Nowcast') plt.plot(time, obs_data, label='Observations') plt.plot(time[15:], res+nowcast_data[15:], label='Corrected nowcast') plt.legend()
Out[73]:
<matplotlib.legend.Legend at 0x14c2c4f79668>
In [ ]:
