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+{
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+ "cells": [
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+ {
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+ "cell_type": "code",
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+ "execution_count": 1,
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+ "id": "5fee9b7a",
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+ "metadata": {},
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+ "outputs": [],
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+ "source": [
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+ "%matplotlib inline\n",
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+ "import os\n",
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+ "import time\n",
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+ "import jax\n",
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+ "import jax.numpy as jnp\n",
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+ "import optax\n",
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+ "import matplotlib.pyplot as plt\n",
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+ "from tqdm import trange\n",
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+ "from jax import jvp, value_and_grad\n",
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+ "from flax import linen as nn\n",
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+ "from typing import Sequence\n",
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+ "from functools import partial\n",
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+ "#from jax import jvp, vjp\n"
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+ ]
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+ },
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+ {
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+ "cell_type": "code",
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+ "execution_count": 2,
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+ "id": "b2db3a40",
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+ "metadata": {},
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+ "outputs": [],
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+ "source": [
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+ "class CPPINN(nn.Module):\n",
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+ " features: Sequence[int]\n",
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+ " \n",
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+ " #bases = [bases_x,bases_y,bases_z]\n",
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+ " @nn.compact\n",
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+ " def __call__(self, a, b, c, d,e):\n",
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+ " inputs, outputs = [a, b, c, d,e], []\n",
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+ " init = nn.initializers.xavier_uniform()\n",
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+ " #s = 0\n",
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+ " for X in inputs:\n",
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+ " #print(s)\n",
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+ " #s = s+1\n",
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+ " for fs in self.features[:-1]:\n",
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+ " X = nn.Dense(fs, kernel_init=init)(X)\n",
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+ " X = nn.activation.tanh(X)\n",
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+ " X = nn.Dense(self.features[-1], kernel_init=init)(X)\n",
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+ " \n",
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+ " outputs += [jnp.transpose(X, (1, 0))]\n",
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+ "\n",
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+ " #xy = jnp.einsum('fx, fy->fxy', outputs[0], outputs[1])\n",
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+ " return jnp.einsum('ra, rb,rc,rd,re->abcde', outputs[0], outputs[1],outputs[2],outputs[3],outputs[4])\n",
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+ "class TTPINN(nn.Module):\n",
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+ " features: Sequence[int]\n",
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+ " \n",
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+ " #bases = [bases_x,bases_y,bases_z]\n",
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+ " @nn.compact\n",
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+ " def __call__(self, a, b, c, d,e):\n",
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+ " inputs, outputs = [a, b, c, d,e], []\n",
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+ " init = nn.initializers.xavier_uniform()\n",
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+ " for i,X in enumerate(inputs):\n",
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+ " for fs in self.features[:-1]:\n",
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+ " X = nn.Dense(fs, kernel_init=init)(X)\n",
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+ " X = nn.activation.tanh(X)\n",
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+ " if i!=0 and i!=4:\n",
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+ " X = nn.DenseGeneral((self.features[-1],self.features[-1]), kernel_init=init)(X)\n",
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+ " else:\n",
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+ " X = nn.Dense(self.features[-1], kernel_init=init)(X)\n",
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+ " outputs += [X]\n",
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+ " return jnp.einsum('a1,b12,c23,d34,e4->abcde',outputs[0],outputs[1],outputs[2],outputs[3],outputs[4])\n",
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+ " \n",
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+ " \n",
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+ "class TuckerPINN(nn.Module):\n",
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+ " features: Sequence[int]\n",
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+ " \n",
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+ " def setup(self):\n",
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+ " # Initialize learnable parameters\n",
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+ " #self.centres = self.param('centres', nn.initializers.uniform(1.01), (self.out_features, 1))\n",
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+ " self.core = self.param(\"core\",nn.initializers.orthogonal(),(self.features[-1],self.features[-1],self.features[-1],self.features[-1],self.features[-1]))\n",
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+ "\n",
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+ " @nn.compact\n",
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+ " def __call__(self, a,b,c,d,e):\n",
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+ " inputs, outputs = [a,b,c,d,e], []\n",
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+ " init = nn.initializers.xavier_normal()\n",
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+ " for X in inputs:\n",
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+ " for fs in self.features[:-1]:\n",
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+ " X = nn.Dense(fs, kernel_init=init)(X)\n",
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+ " X = nn.activation.tanh(X)\n",
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+ " X = nn.Dense(self.features[-1], kernel_init=init)(X)\n",
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+ " \n",
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+ " outputs += [jnp.transpose(X, (1, 0))]\n",
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+ " #mid = jnp.einsum(\"fx,fy->fxy\",outputs[0],outputs[1])\n",
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+ " return jnp.einsum(\"klmno,ka,lb,mc,nd,oe->abcde\",self.core,outputs[0],outputs[1],outputs[2],outputs[3],outputs[4])\n",
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+ " \n",
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+ " \n",
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+ " \n"
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+ ]
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+ },
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+ {
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+ "cell_type": "code",
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+ "execution_count": 3,
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+ "id": "9ed5b134",
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+ "metadata": {},
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+ "outputs": [],
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+ "source": [
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+ "def hvp_fwdfwd(f, primals, tangents, return_primals=False):\n",
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+ " g = lambda primals: jvp(f, (primals,), tangents)[1]\n",
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+ " primals_out, tangents_out = jvp(g, primals, tangents)\n",
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+ " if return_primals:\n",
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+ " return primals_out, tangents_out\n",
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+ " else:\n",
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+ " return tangents_out\n",
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+ " \n",
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+ "\n"
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+ ]
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+ },
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+ {
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+ "cell_type": "code",
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+ "execution_count": 4,
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+ "id": "bd7e36fa",
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+ "metadata": {},
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+ "outputs": [],
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+ "source": [
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+ "def loss_poisson(apply_fn, *train_data):\n",
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+ " def residual_loss(params,a,b,c,d,e,source):\n",
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+ " \n",
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+ " u = apply_fn(params,a,b,c,d,e)\n",
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+ " # tangent vector dx/dx\n",
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+ " #v_f = jnp.ones(f.shape)\n",
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+ " v_a = jnp.ones(a.shape)\n",
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+ " v_b = jnp.ones(b.shape)\n",
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+ " v_c = jnp.ones(c.shape)\n",
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+ " v_d = jnp.ones(d.shape)\n",
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+ " v_e = jnp.ones(e.shape)\n",
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+ " #source = 0\n",
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+ " #for i in [a,b,c,d,e,t]:\n",
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+ " #source = source + jnp.sin(jnp.pi/2 * i)\n",
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+ "\n",
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+ " uaa = hvp_fwdfwd(lambda a: apply_fn(params,a,b,c,d,e), (a,), (v_a,))\n",
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+ " ubb = hvp_fwdfwd(lambda b: apply_fn(params,a,b,c,d,e), (b,), (v_b,))\n",
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+ " ucc = hvp_fwdfwd(lambda c: apply_fn(params,a,b,c,d,e), (c,), (v_c,))\n",
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+ " udd = hvp_fwdfwd(lambda d: apply_fn(params,a,b,c,d,e), (d,), (v_d,))\n",
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+ " uee = hvp_fwdfwd(lambda e: apply_fn(params,a,b,c,d,e), (e,), (v_e,))\n",
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+ " #uff = hvp_fwdfwd(lambda t: apply_fn(params,a,b,c,d,e,f), (f,), (v_f,))\n",
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+ " nabla_u = uaa + ubb + ucc + udd + uee \n",
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+ " return jnp.mean((nabla_u + source)**2)\n",
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+ " def boundary_loss(params,a,b,c,d,e,u):\n",
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+ " loss = 0\n",
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+ " for i in range(10):\n",
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+ " loss += jnp.mean((apply_fn(params,a[i],b[i],c[i],d[i],e[i])-u[i])**2)\n",
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+ " return loss\n",
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+ " ac,bc,cc,dc,ec,source_term,ab,bb,cb,db,eb,ub = train_data\n",
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+ " loss_fn = lambda params: residual_loss(params,ac,bc,cc,dc,ec,source_term ) + boundary_loss(params,ab,bb,cb,db,eb,ub)\n",
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+ " return loss_fn\n",
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+ "\n",
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+ "\n",
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+ "# optimizer step function\n",
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+ "@partial(jax.jit, static_argnums=(0,))\n",
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+ "def update_model(optim, gradient, params, state):\n",
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+ " updates, state = optim.update(gradient, state)\n",
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+ " params = optax.apply_updates(params, updates)\n",
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+ " return params, state\n",
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+ "\n",
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+ " "
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+ ]
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+ },
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+ {
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+ "cell_type": "code",
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+ "execution_count": 5,
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+ "id": "78f894c9",
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+ "metadata": {},
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+ "outputs": [],
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+ "source": [
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+ "def poisson_exact(a,b,c,d,e):\n",
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+ " sol = 0\n",
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+ " for i in [a,b,c,d,e]:\n",
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+ " sol += jnp.sin((jnp.pi/2) * i) \n",
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+ " return sol\n",
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+ "\n",
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+ "def relative_l2(u, u_gt):\n",
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+ " return jnp.linalg.norm(u-u_gt)/jnp.linalg.norm(u_gt)"
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+ ]
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+ },
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+ {
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+ "cell_type": "code",
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+ "execution_count": 6,
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+ "id": "78e5c4ef",
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+ "metadata": {},
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+ "outputs": [],
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+ "source": [
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+ "def train_generator(nc,key):\n",
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+ " keys = jax.random.split(key, 6)\n",
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+ " ac = jax.random.uniform(keys[0], (nc,), minval=0., maxval=1.)\n",
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+ " bc = jax.random.uniform(keys[1], (nc,), minval=0., maxval=1.)\n",
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+ " cc = jax.random.uniform(keys[2], (nc,), minval=0., maxval=1.)\n",
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+ " dc = jax.random.uniform(keys[3], (nc,), minval=0., maxval=1.)\n",
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+ " ec = jax.random.uniform(keys[4], (nc,), minval=0., maxval=1.)\n",
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+ " #fc = jax.random.uniform(keys[5], (nc,), minval=0., maxval=1.)\n",
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+ " \n",
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+ " acm,bcm,ccm,dcm,ecm = jnp.meshgrid(ac,bc,cc,dc,ec, indexing='ij')\n",
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+ " source_term = 0\n",
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+ " for i in [acm,bcm,ccm,dcm,ecm]:\n",
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+ " source_term = source_term + ((jnp.pi*jnp.pi/4) * jnp.sin((jnp.pi/2) * i))\n",
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+ " \n",
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+ " #print(\"Soucr shape: \",source_term.shape)\n",
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+ "\n",
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+ " ac = ac.reshape(-1, 1)\n",
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+ " bc = bc.reshape(-1, 1)\n",
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+ " cc = cc.reshape(-1, 1)\n",
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+ " dc = dc.reshape(-1, 1)\n",
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+ " ec = ec.reshape(-1, 1)\n",
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+ " #fc = fc.reshape(-1, 1) \n",
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+ " \n",
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+ " \n",
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+ " ab = [jnp.array([[0.]]), jnp.array([[1.]]),ac,ac,ac,ac,ac,ac,ac,ac]\n",
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+ " bb = [bc,bc,jnp.array([[0.]]), jnp.array([[1.]]),bc,bc,bc,bc,bc,bc]\n",
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+ " cb = [cc,cc,cc,cc,jnp.array([[0.]]), jnp.array([[1.]]),cc,cc,cc,cc]\n",
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+ " db = [dc,dc,dc,dc,dc,dc,jnp.array([[0.]]), jnp.array([[1.]]),dc,dc]\n",
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+ " eb = [ec,ec,ec,ec,ec,ec,ec,ec,jnp.array([[0.]]), jnp.array([[1.]])]\n",
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+ " #fb = [fc,fc,fc,fc,fc,fc,fc,fc,fc,fc,jnp.array([[0.]]), jnp.array([[1.]])]\n",
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+ " ub = []\n",
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+ " for i in range(10):\n",
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+ " abm,bbm,cbm,dbm,ebm = jnp.meshgrid(ab[i].ravel(),bb[i].ravel(),cb[i].ravel(),db[i].ravel(),eb[i].ravel(), indexing = \"ij\")\n",
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+ " ub += [poisson_exact(abm,bbm,cbm,dbm,ebm)]\n",
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+ " \n",
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+ " return ac,bc,cc,dc,ec,source_term,ab,bb,cb,db,eb,ub \n",
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+ "def test_generator(nc_test):\n",
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+ " a = jnp.linspace(0,1,nc_test)\n",
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+ " b = jnp.linspace(0,1,nc_test)\n",
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+ " c = jnp.linspace(0,1,nc_test)\n",
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+ " d = jnp.linspace(0,1,nc_test)\n",
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+ " e = jnp.linspace(0,1,nc_test)\n",
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+ " #f = jnp.linspace(0,1,nc_test)\n",
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+ " \n",
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+ " am,bm,cm,dm,em = jnp.meshgrid(a,b,c,d,e, indexing='ij')\n",
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+ " \n",
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+ " u_gt = poisson_exact(am,bm,cm,dm,em)\n",
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+ " \n",
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+ " a = a.reshape(-1,1)\n",
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+ " b = b.reshape(-1,1)\n",
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+ " c = c.reshape(-1,1)\n",
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+ " d = d.reshape(-1,1)\n",
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+ " e = e.reshape(-1,1)\n",
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+ " #f = f.reshape(-1,1)\n",
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+ " \n",
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+ " return a,b,c,d,e,am,bm,cm,dm,em,u_gt\n",
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+ "\n",
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+ "\n",
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+ " \n",
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+ " "
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+ ]
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+ },
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+ {
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+ "cell_type": "code",
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+ "execution_count": 7,
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+ "id": "0064839a",
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+ "metadata": {},
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+ "outputs": [],
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+ "source": [
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+ "def main(mode,NC, NI, NB, NC_TEST, SEED, LR, EPOCHS, N_LAYERS, FEATURES, LOG_ITER):\n",
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+ " # force jax to use one device\n",
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+ " os.environ[\"CUDA_VISIBLE_DEVICES\"]=\"0\"\n",
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+ " os.environ[\"XLA_PYTHON_CLIENT_PREALLOCATE\"]=\"false\"\n",
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+ "\n",
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+ " # random key\n",
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+ " key = jax.random.PRNGKey(SEED)\n",
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+ " key, subkey = jax.random.split(key, 2)\n",
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+ "\n",
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+ " # feature sizes\n",
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+ " feat_sizes = tuple(FEATURES for _ in range(N_LAYERS))\n",
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+ "\n",
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+ " #model = RBFPINN(FEATURES,linear)\n",
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+ " # make & init model\n",
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+ " if mode == \"CPPINN\":\n",
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+ " model = CPPINN(feat_sizes)\n",
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+ " elif mode == \"TTPINN\":\n",
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+ " model = TTPINN(feat_sizes)\n",
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+ " elif mode == \"TuckerPINN\":\n",
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+ " model = TuckerPINN(feat_sizes)\n",
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+ " params = model.init(subkey, jax.random.uniform(key,(NC, 1)), jax.random.uniform(key,(NC, 1)),jax.random.uniform(key,(NC, 1)),jax.random.uniform(key,(NC, 1)),jax.random.uniform(key,(NC, 1)))\n",
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+ " # optimizer\n",
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+ " optim = optax.adam(LR)\n",
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+ " state = optim.init(params)\n",
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+ " \n",
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+ " key, subkey = jax.random.split(key, 2)\n",
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+ " train_data = train_generator(NC, subkey)\n",
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+ " \n",
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+ " a,b,c,d,e,am,bm,cm,dm,em,u_gt = test_generator(NC_TEST)\n",
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+ " #print(u_gt.shape)\n",
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+ " logger = []\n",
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+ " \n",
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+ " apply_fn = jax.jit(model.apply)\n",
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+ " #print(len(*train_data))\n",
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+ " loss_fn = loss_poisson(apply_fn, *train_data)\n",
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+ " print(loss_fn)\n",
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+ " \n",
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+ " @jax.jit\n",
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+ " def train_one_step(params, state):\n",
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+ " # compute loss and gradient\n",
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+ " loss, gradient = value_and_grad(loss_fn)(params)\n",
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+ " # update state\n",
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+ " params, state = update_model(optim, gradient, params, state)\n",
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+ " return loss, params, state\n",
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+ " \n",
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+ " start = time.time()\n",
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+ " \n",
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+ " for iters in trange(1, EPOCHS+1):\n",
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+ " # single run\n",
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+ " loss, params, state = train_one_step(params, state)\n",
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+ " #print(\"its all good until,\",e)\n",
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+ " if iters % LOG_ITER == 0 or iters == 1:\n",
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+ " #print(e)\n",
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+ " u = apply_fn(params,a,b,c,d,e)\n",
|
|
|
+ " \n",
|
|
|
+ " error = relative_l2(u, u_gt)\n",
|
|
|
+ " print(f'Epoch: {iters}/{EPOCHS} --> loss: {loss:.8f}, error: {error:.8f}')\n",
|
|
|
+ " logger.append([iters,loss,error])\n",
|
|
|
+ " #print('Solution:')\n",
|
|
|
+ " #u = apply_fn(params, x,y,z)\n",
|
|
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+ " #plotter(xm,ym,zm,u)\n",
|
|
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+ " \n",
|
|
|
+ " end = time.time()\n",
|
|
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+ " print(f'Runtime: {((end-start)/EPOCHS*1000):.2f} ms/iter.')\n",
|
|
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+ "\n",
|
|
|
+ " #print('Solution:')\n",
|
|
|
+ " #äu = apply_fn(params, x,y,z)\n",
|
|
|
+ " #plotter(xm,ym,zm,u)\n",
|
|
|
+ " return logger\n"
|
|
|
+ ]
|
|
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+ },
|
|
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+ {
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+ "cell_type": "code",
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+ "execution_count": 8,
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+ "id": "f83644e2",
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+ "metadata": {},
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+ "outputs": [
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+ {
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+ "name": "stderr",
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+ "output_type": "stream",
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+ "text": [
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+ "2024-06-12 11:27:36.567754: W external/xla/xla/service/gpu/nvptx_compiler.cc:760] The NVIDIA driver's CUDA version is 12.2 which is older than the ptxas CUDA version (12.5.40). Because the driver is older than the ptxas version, XLA is disabling parallel compilation, which may slow down compilation. You should update your NVIDIA driver or use the NVIDIA-provided CUDA forward compatibility packages.\n"
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+ ]
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+ },
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+ {
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+ "name": "stdout",
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+ "output_type": "stream",
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+ "text": [
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+ "<function loss_poisson.<locals>.<lambda> at 0x7efb941e39c0>\n"
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+ ]
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+ },
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+ {
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+ "name": "stderr",
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+ "output_type": "stream",
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+ "text": [
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+ " 0%| | 25/80000 [00:10<6:54:56, 3.21it/s]"
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+ ]
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+ },
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+ {
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+ "name": "stdout",
|
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+ "output_type": "stream",
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+ "text": [
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+ "Epoch: 1/80000 --> loss: 73.37992096, error: 0.99991661\n"
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+ ]
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+ },
|
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+ {
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+ "name": "stderr",
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+ "output_type": "stream",
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+ "text": [
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+ " 6%|█████▌ | 5022/80000 [00:30<05:10, 241.86it/s]"
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+ ]
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+ },
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+ {
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+ "name": "stdout",
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+ "output_type": "stream",
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+ "text": [
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+ "Epoch: 5000/80000 --> loss: 0.01108064, error: 0.17494334\n"
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+ ]
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+ },
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+ {
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+ "name": "stderr",
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+ "output_type": "stream",
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+ "text": [
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+ " 13%|██████████▉ | 10047/80000 [00:50<04:29, 260.00it/s]"
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+ ]
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+ },
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+ {
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+ "name": "stdout",
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+ "output_type": "stream",
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+ "text": [
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+ "Epoch: 10000/80000 --> loss: 0.02779304, error: 0.26750535\n"
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+ ]
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+ },
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+ {
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+ "name": "stderr",
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+ "output_type": "stream",
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+ "text": [
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+ " 19%|████████████████▎ | 15035/80000 [01:10<04:06, 263.12it/s]"
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+ ]
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+ },
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+ {
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+ "name": "stdout",
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+ "output_type": "stream",
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+ "text": [
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+ "Epoch: 15000/80000 --> loss: 0.00336808, error: 0.15429279\n"
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+ ]
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+ },
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+ {
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+ "name": "stderr",
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+ "output_type": "stream",
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+ "text": [
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+ " 25%|█████████████████████▊ | 20039/80000 [01:31<04:24, 226.42it/s]"
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+ ]
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+ },
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+ {
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+ "name": "stdout",
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+ "output_type": "stream",
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+ "text": [
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+ "Epoch: 20000/80000 --> loss: 0.00126985, error: 0.09762570\n"
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+ ]
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+ },
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+ {
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+ "name": "stderr",
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+ "output_type": "stream",
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+ "text": [
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+ " 31%|███████████████████████████▏ | 25037/80000 [01:51<03:47, 241.18it/s]"
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+ ]
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+ {
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+ "name": "stdout",
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+ "output_type": "stream",
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+ "text": [
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+ "Epoch: 25000/80000 --> loss: 0.00106328, error: 0.07395156\n"
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+ ]
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+ },
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+ {
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+ "name": "stderr",
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+ "output_type": "stream",
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+ "text": [
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+ " 38%|████████████████████████████████▋ | 30039/80000 [02:13<03:10, 261.60it/s]"
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+ ]
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+ "name": "stdout",
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+ "output_type": "stream",
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+ "text": [
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+ "Epoch: 30000/80000 --> loss: 0.00067216, error: 0.06847223\n"
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+ ]
|
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+ },
|
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+ {
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+ "name": "stderr",
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+ "output_type": "stream",
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+ "text": [
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+ " 44%|██████████████████████████████████████ | 35019/80000 [02:34<04:09, 180.38it/s]"
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+ ]
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+ },
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+ {
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+ "name": "stdout",
|
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+ "output_type": "stream",
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+ "text": [
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+ "Epoch: 35000/80000 --> loss: 0.00896472, error: 0.05443084\n"
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+ ]
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+ },
|
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+ {
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+ "name": "stderr",
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+ "output_type": "stream",
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+ "text": [
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+ " 50%|███████████████████████████████████████████▌ | 40041/80000 [02:55<02:43, 244.73it/s]"
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+ ]
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+ },
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+ {
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+ "name": "stdout",
|
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+ "output_type": "stream",
|
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+ "text": [
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+ "Epoch: 40000/80000 --> loss: 0.00016538, error: 0.07235554\n"
|
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+ ]
|
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+ },
|
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+ {
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+ "name": "stderr",
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+ "output_type": "stream",
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+ "text": [
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+ " 56%|████████████████████████████████████████████████▉ | 45051/80000 [03:15<02:18, 252.95it/s]"
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+ ]
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+ },
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+ {
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+ "name": "stdout",
|
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+ "output_type": "stream",
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+ "text": [
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+ "Epoch: 45000/80000 --> loss: 0.00013811, error: 0.08078795\n"
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+ ]
|
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+ },
|
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+ {
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+ "name": "stderr",
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+ "output_type": "stream",
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+ "text": [
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+ " 63%|██████████████████████████████████████████████████████▍ | 50030/80000 [03:35<02:20, 212.59it/s]"
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+ ]
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+ },
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+ {
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+ "name": "stdout",
|
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+ "output_type": "stream",
|
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+ "text": [
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+ "Epoch: 50000/80000 --> loss: 0.00012048, error: 0.08825949\n"
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+ ]
|
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+ },
|
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+ {
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+ "name": "stderr",
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+ "output_type": "stream",
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+ "text": [
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+ " 69%|███████████████████████████████████████████████████████████▊ | 55028/80000 [03:57<02:09, 193.07it/s]"
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+ ]
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+ },
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+ {
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+ "name": "stdout",
|
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+ "output_type": "stream",
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+ "text": [
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+ "Epoch: 55000/80000 --> loss: 0.00010529, error: 0.09537643\n"
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+ ]
|
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+ },
|
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+ {
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+ "name": "stderr",
|
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+ "output_type": "stream",
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+ "text": [
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+ " 75%|█████████████████████████████████████████████████████████████████▎ | 60045/80000 [04:20<01:21, 245.99it/s]"
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+ ]
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+ },
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+ {
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+ "name": "stdout",
|
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+ "output_type": "stream",
|
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+ "text": [
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+ "Epoch: 60000/80000 --> loss: 0.00024248, error: 0.10090642\n"
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+ ]
|
|
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+ },
|
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+ {
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+ "name": "stderr",
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+ "output_type": "stream",
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+ "text": [
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+ " 81%|██████████████████████████████████████████████████████████████████████▋ | 65040/80000 [04:41<01:02, 238.85it/s]"
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+ ]
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+ },
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+ {
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+ "name": "stdout",
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+ "output_type": "stream",
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+ "text": [
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+ "Epoch: 65000/80000 --> loss: 0.00032162, error: 0.10354331\n"
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+ ]
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+ },
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+ {
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+ "name": "stderr",
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+ "output_type": "stream",
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+ "text": [
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+ " 88%|████████████████████████████████████████████████████████████████████████████▏ | 70049/80000 [05:01<00:38, 259.67it/s]"
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+ ]
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+ },
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+ "name": "stdout",
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+ "output_type": "stream",
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+ "text": [
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+ "Epoch: 70000/80000 --> loss: 0.00008225, error: 0.10996926\n"
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+ ]
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+ },
|
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+ {
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+ "name": "stderr",
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+ "output_type": "stream",
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+ "text": [
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+ " 94%|█████████████████████████████████████████████████████████████████████████████████▌ | 75030/80000 [05:21<00:20, 247.23it/s]"
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+ ]
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+ },
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+ {
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+ "name": "stdout",
|
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+ "output_type": "stream",
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+ "text": [
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+ "Epoch: 75000/80000 --> loss: 0.00007818, error: 0.11272150\n"
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+ ]
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+ },
|
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+ {
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+ "name": "stderr",
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+ "output_type": "stream",
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+ "text": [
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+ "100%|███████████████████████████████████████████████████████████████████████████████████████| 80000/80000 [05:42<00:00, 233.88it/s]\n"
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+ ]
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+ },
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+ {
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+ "name": "stdout",
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+ "output_type": "stream",
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+ "text": [
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+ "Epoch: 80000/80000 --> loss: 0.00007414, error: 0.11545516\n",
|
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+ "Runtime: 4.28 ms/iter.\n",
|
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+ "<function loss_poisson.<locals>.<lambda> at 0x7efb9405a8e0>\n"
|
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+ ]
|
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+ },
|
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+ {
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+ "name": "stderr",
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+ "output_type": "stream",
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+ "text": [
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+ " 0%| | 29/80000 [00:09<5:10:16, 4.30it/s]"
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+ ]
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+ },
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+ {
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+ "name": "stdout",
|
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+ "output_type": "stream",
|
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+ "text": [
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+ "Epoch: 1/80000 --> loss: 70.25621033, error: 0.99982649\n"
|
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|
+ ]
|
|
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+ },
|
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+ {
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+ "name": "stderr",
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+ "output_type": "stream",
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+ "text": [
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+ " 6%|█████▌ | 5042/80000 [00:30<05:32, 225.53it/s]"
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+ ]
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+ },
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+ {
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+ "name": "stdout",
|
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+ "output_type": "stream",
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+ "text": [
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+ "Epoch: 5000/80000 --> loss: 0.00661923, error: 0.61806887\n"
|
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+ ]
|
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+ },
|
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+ {
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+ "name": "stderr",
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+ "output_type": "stream",
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+ "text": [
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+ " 13%|██████████▉ | 10044/80000 [00:51<04:28, 260.87it/s]"
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+ ]
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+ },
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+ {
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+ "name": "stdout",
|
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+ "output_type": "stream",
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+ "text": [
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+ "Epoch: 10000/80000 --> loss: 0.00247230, error: 0.43392652\n"
|
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+ ]
|
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+ },
|
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+ {
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+ "name": "stderr",
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+ "output_type": "stream",
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+ "text": [
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+ " 19%|████████████████▎ | 15031/80000 [01:11<04:11, 258.11it/s]"
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+ ]
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+ },
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+ {
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+ "name": "stdout",
|
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+ "output_type": "stream",
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+ "text": [
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+ "Epoch: 15000/80000 --> loss: 0.00093180, error: 0.23548518\n"
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+ ]
|
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+ },
|
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+ {
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+ "name": "stderr",
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+ "output_type": "stream",
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+ "text": [
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+ " 25%|█████████████████████▊ | 20035/80000 [01:30<03:52, 257.77it/s]"
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+ ]
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+ },
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+ {
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+ "name": "stdout",
|
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+ "output_type": "stream",
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+ "text": [
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+ "Epoch: 20000/80000 --> loss: 0.00044429, error: 0.15038651\n"
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+ ]
|
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+ },
|
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+ {
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+ "name": "stderr",
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+ "output_type": "stream",
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+ "text": [
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+ " 31%|███████████████████████████▏ | 25043/80000 [01:50<03:33, 257.83it/s]"
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+ ]
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+ },
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+ {
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+ "name": "stdout",
|
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+ "output_type": "stream",
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+ "text": [
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+ "Epoch: 25000/80000 --> loss: 0.00163372, error: 0.11389064\n"
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+ ]
|
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+ },
|
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+ {
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+ "name": "stderr",
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+ "output_type": "stream",
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+ "text": [
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+ " 38%|████████████████████████████████▋ | 30030/80000 [02:09<03:14, 256.99it/s]"
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+ ]
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+ },
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+ {
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+ "name": "stdout",
|
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+ "output_type": "stream",
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+ "text": [
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+ "Epoch: 30000/80000 --> loss: 0.00013692, error: 0.12747999\n"
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+ ]
|
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+ },
|
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+ {
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+ "name": "stderr",
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+ "output_type": "stream",
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+ "text": [
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+ " 44%|██████████████████████████████████████ | 35039/80000 [02:29<02:55, 255.48it/s]"
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+ ]
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+ },
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+ {
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+ "name": "stdout",
|
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+ "output_type": "stream",
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+ "text": [
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+ "Epoch: 35000/80000 --> loss: 0.00011111, error: 0.13291767\n"
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+ ]
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+ },
|
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+ {
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+ "name": "stderr",
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+ "output_type": "stream",
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+ "text": [
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+ " 88%|█████████████████████████████████████████████████████████████████████████████ | 70008/80000 [21:18<02:57, 56.18it/s]"
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+ " 94%|██████████████████████████████████████████████████████████████████████████████████▌ | 75008/80000 [22:50<01:29, 55.69it/s]"
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+ "Runtime: 18.27 ms/iter.\n",
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+ "<function loss_poisson.<locals>.<lambda> at 0x7ef9b43a6200>\n"
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+ " 0%| | 9/80000 [00:04<8:52:52, 2.50it/s]"
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+ " 19%|████████████████▌ | 15008/80000 [04:38<19:26, 55.72it/s]"
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+ "text": [
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+ "Epoch: 15000/80000 --> loss: 0.00225159, error: 0.32316279\n"
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+ ]
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+ },
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+ "text": [
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+ " 25%|██████████████████████ | 20008/80000 [06:09<18:05, 55.29it/s]"
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+ "name": "stdout",
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+ "Epoch: 20000/80000 --> loss: 0.00038382, error: 0.23823363\n"
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+ " 31%|███████████████████████████▌ | 25008/80000 [07:41<16:20, 56.07it/s]"
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+ "Epoch: 25000/80000 --> loss: 0.00021448, error: 0.20547117\n"
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+ "Epoch: 30000/80000 --> loss: 0.00013199, error: 0.17643313\n"
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+ " 44%|██████████████████████████████████████▌ | 35008/80000 [10:43<13:23, 56.02it/s]"
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+ "name": "stdout",
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+ "text": [
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+ "Epoch: 35000/80000 --> loss: 0.00050769, error: 0.15873562\n"
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+ ]
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+ " 50%|████████████████████████████████████████████ | 40008/80000 [12:14<11:46, 56.64it/s]"
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+ "name": "stdout",
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+ "Epoch: 40000/80000 --> loss: 0.00040635, error: 0.15193489\n"
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+ ]
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+ },
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+ "text": [
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+ " 56%|█████████████████████████████████████████████████▌ | 45008/80000 [13:45<10:19, 56.52it/s]"
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+ ]
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+ "text": [
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+ "Epoch: 45000/80000 --> loss: 0.00004433, error: 0.13278601\n"
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+ ]
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+ {
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+ "name": "stderr",
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+ "text": [
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+ " 63%|███████████████████████████████████████████████████████ | 50008/80000 [15:16<08:54, 56.07it/s]"
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+ ]
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+ "name": "stdout",
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+ "output_type": "stream",
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+ "text": [
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+ "Epoch: 50000/80000 --> loss: 0.00015471, error: 0.11521255\n"
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+ ]
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+ },
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+ {
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+ "name": "stderr",
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+ "output_type": "stream",
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+ "text": [
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+ " 69%|████████████████████████████████████████████████████████████▌ | 55008/80000 [16:47<07:27, 55.89it/s]"
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+ ]
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+ },
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+ {
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+ "name": "stdout",
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+ "output_type": "stream",
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+ "text": [
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+ "Epoch: 55000/80000 --> loss: 0.00003695, error: 0.10423762\n"
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+ ]
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+ },
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+ {
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+ "name": "stderr",
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+ "output_type": "stream",
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+ "text": [
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+ " 75%|██████████████████████████████████████████████████████████████████ | 60008/80000 [18:18<06:02, 55.18it/s]"
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+ "name": "stdout",
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+ "output_type": "stream",
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+ "text": [
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+ "Epoch: 60000/80000 --> loss: 0.00004013, error: 0.09015504\n"
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+ ]
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+ },
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+ {
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+ "name": "stderr",
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+ "output_type": "stream",
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+ "text": [
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+ " 81%|███████████████████████████████████████████████████████████████████████▌ | 65008/80000 [19:50<04:29, 55.67it/s]"
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+ "name": "stdout",
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+ "output_type": "stream",
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+ "text": [
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+ "Epoch: 65000/80000 --> loss: 0.00039381, error: 0.07617349\n"
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+ ]
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+ },
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+ {
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+ "name": "stderr",
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+ "output_type": "stream",
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+ "text": [
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+ " 88%|█████████████████████████████████████████████████████████████████████████████ | 70008/80000 [21:21<03:00, 55.44it/s]"
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+ ]
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+ },
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+ {
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+ "name": "stdout",
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+ "output_type": "stream",
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+ "text": [
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+ "Epoch: 70000/80000 --> loss: 0.00726376, error: 0.05860844\n"
|
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+ ]
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+ },
|
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+ {
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+ "name": "stderr",
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+ "output_type": "stream",
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+ "text": [
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+ " 94%|██████████████████████████████████████████████████████████████████████████████████▌ | 75008/80000 [22:52<01:29, 56.01it/s]"
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+ ]
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+ },
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+ {
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|
+ "name": "stdout",
|
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|
+ "output_type": "stream",
|
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+ "text": [
|
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+ "Epoch: 75000/80000 --> loss: 0.00004069, error: 0.05802687\n"
|
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+ ]
|
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+ },
|
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+ {
|
|
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+ "name": "stderr",
|
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+ "output_type": "stream",
|
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+ "text": [
|
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+ "100%|████████████████████████████████████████████████████████████████████████████████████████| 80000/80000 [24:23<00:00, 54.65it/s]"
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+ ]
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+ },
|
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+ {
|
|
|
+ "name": "stdout",
|
|
|
+ "output_type": "stream",
|
|
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+ "text": [
|
|
|
+ "Epoch: 80000/80000 --> loss: 0.00067186, error: 0.05043495\n",
|
|
|
+ "Runtime: 18.30 ms/iter.\n"
|
|
|
+ ]
|
|
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+ },
|
|
|
+ {
|
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|
+ "name": "stderr",
|
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+ "output_type": "stream",
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+ "text": [
|
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+ "\n"
|
|
|
+ ]
|
|
|
+ }
|
|
|
+ ],
|
|
|
+ "source": [
|
|
|
+ "import numpy as np \n",
|
|
|
+ "num = 24\n",
|
|
|
+ "for model in [\"CPPINN\",\"TTPINN\",\"TuckerPINN\"]:\n",
|
|
|
+ " for rank in [6,8,12]:\n",
|
|
|
+ " for run in range(10):\n",
|
|
|
+ " logs = main(mode=model,NC=num, NI=num, NB=num, NC_TEST=32, SEED=444444+run, LR=1e-3, EPOCHS=80000, N_LAYERS=4, FEATURES=rank, LOG_ITER=5000)\n",
|
|
|
+ " logs = np.array(logs)\n",
|
|
|
+ " if np.min(logs[:,2]) < 0.1:\n",
|
|
|
+ " np.savetxt(\"Rank{}/Model_{}_points_{}_run_{}\".format(rank,model,num,run),logs)\n",
|
|
|
+ " "
|
|
|
+ ]
|
|
|
+ },
|
|
|
+ {
|
|
|
+ "cell_type": "code",
|
|
|
+ "execution_count": null,
|
|
|
+ "id": "280bf139",
|
|
|
+ "metadata": {},
|
|
|
+ "outputs": [],
|
|
|
+ "source": []
|
|
|
+ }
|
|
|
+ ],
|
|
|
+ "metadata": {
|
|
|
+ "kernelspec": {
|
|
|
+ "display_name": "jaxdf",
|
|
|
+ "language": "python",
|
|
|
+ "name": "jaxdf"
|
|
|
+ },
|
|
|
+ "language_info": {
|
|
|
+ "codemirror_mode": {
|
|
|
+ "name": "ipython",
|
|
|
+ "version": 3
|
|
|
+ },
|
|
|
+ "file_extension": ".py",
|
|
|
+ "mimetype": "text/x-python",
|
|
|
+ "name": "python",
|
|
|
+ "nbconvert_exporter": "python",
|
|
|
+ "pygments_lexer": "ipython3",
|
|
|
+ "version": "3.12.2"
|
|
|
+ }
|
|
|
+ },
|
|
|
+ "nbformat": 4,
|
|
|
+ "nbformat_minor": 5
|
|
|
+}
|
