estimator inference files

ArspecExtract.py

@@ -0,0 +1,246 @@
+from __future__ import absolute_import
+from __future__ import print_function
+import numpy as np
+import wave
+import os
+import math
+from scipy.fftpack.realtransforms import dct
+from copy import deepcopy
+from scipy.fftpack import fft, ifft
+from scikits.talkbox.linpred import lpc
+np.random.seed(1337)
+epsilon = 0.0000000001
+
+
+def build_data(wav, begin=None, end=None):
+    wav_in_file = wave.Wave_read(wav)
+    wav_in_num_samples = wav_in_file.getnframes()
+    N = wav_in_file.getnframes()
+    dstr = wav_in_file.readframes(N)
+    data = np.fromstring(dstr, np.int16)
+    return data
+
+
+def periodogram(x, nfft=None, fs=1):
+    """Compute the periodogram of the given signal, with the given fft size.
+
+    Parameters
+    ----------
+    x : array-like
+        input signal
+    nfft : int
+        size of the fft to compute the periodogram. If None (default), the
+        length of the signal is used. if nfft > n, the signal is 0 padded.
+    fs : float
+        Sampling rate. By default, is 1 (normalized frequency. e.g. 0.5 is the
+        Nyquist limit).
+
+    Returns
+    -------
+    pxx : array-like
+        The psd estimate.
+    fgrid : array-like
+        Frequency grid over which the periodogram was estimated.
+
+    Examples
+    --------
+    Generate a signal with two sinusoids, and compute its periodogram:
+
+    >>> fs = 1000
+    >>> x = np.sin(2 * np.pi  * 0.1 * fs * np.linspace(0, 0.5, 0.5*fs))
+    >>> x += np.sin(2 * np.pi  * 0.2 * fs * np.linspace(0, 0.5, 0.5*fs))
+    >>> px, fx = periodogram(x, 512, fs)
+
+    Notes
+    -----
+    Only real signals supported for now.
+
+    Returns the one-sided version of the periodogram.
+
+    Discrepency with matlab: matlab compute the psd in unit of power / radian /
+    sample, and we compute the psd in unit of power / sample: to get the same
+    result as matlab, just multiply the result from talkbox by 2pi"""
+    x = np.atleast_1d(x)
+    n = x.size
+
+    if x.ndim > 1:
+        raise ValueError("Only rank 1 input supported for now.")
+    if not np.isrealobj(x):
+        raise ValueError("Only real input supported for now.")
+    if not nfft:
+        nfft = n
+    if nfft < n:
+        raise ValueError("nfft < signal size not supported yet")
+
+    pxx = np.abs(fft(x, nfft)) ** 2
+    if nfft % 2 == 0:
+        pn = nfft / 2 + 1
+    else:
+        pn = (nfft + 1) / 2
+
+    fgrid = np.linspace(0, fs * 0.5, pn)
+    return pxx[:pn] / (n * fs), fgrid
+
+
+def arspec(x, order, nfft=None, fs=1):
+    """Compute the spectral density using an AR model.
+
+    An AR model of the signal is estimated through the Yule-Walker equations;
+    the estimated AR coefficient are then used to compute the spectrum, which
+    can be computed explicitely for AR models.
+
+    Parameters
+    ----------
+    x : array-like
+        input signal
+    order : int
+        Order of the LPC computation.
+    nfft : int
+        size of the fft to compute the periodogram. If None (default), the
+        length of the signal is used. if nfft > n, the signal is 0 padded.
+    fs : float
+        Sampling rate. By default, is 1 (normalized frequency. e.g. 0.5 is the
+        Nyquist limit).
+
+    Returns
+    -------
+    pxx : array-like
+        The psd estimate.
+    fgrid : array-like
+        Frequency grid over which the periodogram was estimated.
+    """
+
+    x = np.atleast_1d(x)
+    n = x.size
+
+    if x.ndim > 1:
+        raise ValueError("Only rank 1 input supported for now.")
+    if not np.isrealobj(x):
+        raise ValueError("Only real input supported for now.")
+    if not nfft:
+        nfft = n
+    a, e, k = lpc(x, order)
+
+    # This is not enough to deal correctly with even/odd size
+    if nfft % 2 == 0:
+        pn = nfft / 2 + 1
+    else:
+        pn = (nfft + 1) / 2
+
+    px = 1 / np.fft.fft(a, nfft)[:pn]
+    pxx = np.real(np.conj(px) * px)
+    pxx /= fs / e
+    fx = np.linspace(0, fs * 0.5, pxx.size)
+    return pxx, fx
+
+
+def arspecs(input_wav, order, Atal=False):
+    epsilon = 0.0000000001
+    data = input_wav
+    ar = []
+    ars = arspec(data, order, 4096)
+    for k, l in zip(ars[0], ars[1]):
+        ar.append(math.log(math.sqrt((k ** 2) + (l ** 2))))
+    for val in range(0, len(ar)):
+        if ar[val] == 0.0:
+            ar[val] = deepcopy(epsilon)
+    mspec1 = np.log10(ar)
+    # Use the DCT to 'compress' the coefficients (spectrum -> cepstrum domain)
+    ar = dct(mspec1, type=2, norm='ortho', axis=-1)
+    return ar[:30]
+
+
+def specPS(input_wav, pitch):
+    N = len(input_wav)
+    samps = N / pitch
+    if samps == 0:
+        samps = 1
+    frames = N / samps
+    data = input_wav[0:frames]
+    specs = periodogram(data, nfft=4096)
+    for i in range(1, int(samps)):
+        data = input_wav[frames * i:frames * (i + 1)]
+        peri = periodogram(data, nfft=4096)
+        for sp in range(len(peri[0])):
+            specs[0][sp] += peri[0][sp]
+    for s in range(len(specs[0])):
+        specs[0][s] /= float(samps)
+    peri = []
+    for k, l in zip(specs[0], specs[1]):
+        if k == 0 and l == 0:
+            peri.append(epsilon)
+        else:
+            peri.append(math.log(math.sqrt((k ** 2) + (l ** 2))))
+    # Filter the spectrum through the triangle filterbank
+    mspec = np.log10(peri)
+    # Use the DCT to 'compress' the coefficients (spectrum -> cepstrum domain)
+    ceps = dct(mspec, type=2, norm='ortho', axis=-1)
+    return ceps[:50]
+
+
+def build_single_feature_row(data):
+    lpcs = [8, 9, 10, 11, 12, 13, 14, 15, 16, 17]
+    arr = []
+    periodo = specPS(data, 50)
+    arr.extend(periodo)
+    for j in lpcs:
+        ars = arspecs(data, j)
+        arr.extend(ars)
+    for i in range(len(arr)):
+        if np.isnan(np.float(arr[i])):
+            arr[i] = 0.0
+    return arr
+
+
+def get_y():
+    data = np.load('timit.npy')
+    timit = []
+    for row in data:
+        y = open('Y/' + str(row[0]).replace("timit", "VTRFormants") + ".y").readline().split()
+        arr = []
+        arr.append(float(y[0]))
+        arr.append(float(y[1]))
+        arr.append(float(y[2]))
+        arr.append(float(y[3]))
+        arr.extend(row)
+        timit.append(arr)
+    nump = np.asarray(timit)
+    np.save('timit_train_arspec',nump)
+    return
+
+
+def build_timit_data():
+    arcep_mat = []
+    path = 'X_test/'
+    for file in [f for f in os.listdir(path) if f.endswith('.wav')]:
+        name = file.replace('.wav', '')
+        y = open('Y_test' + '/' + str(name).replace("timit", "VTRFormants") + ".y").readline().split()
+        X = build_data(path + file)
+        arr = [name]
+        arr.append(float(y[0]))
+        arr.append(float(y[1]))
+        arr.append(float(y[2]))
+        arr.append(float(y[3]))
+        arr.extend(build_single_feature_row(X))
+        arcep_mat.append(arr)
+    nump = np.asarray(arcep_mat)
+    np.save('timitTest',nump)
+
+    arcep_mat = []
+    path = 'X/'
+    for file in [f for f in os.listdir(path) if f.endswith('.wav')]:
+        name = file.replace('.wav', '')
+        y = open('Y/' + str(name).replace("timit", "VTRFormants") + ".y").readline().split()
+        X = build_data(path + file)
+        arr = [name]
+        arr.append(float(y[0]))
+        arr.append(float(y[1]))
+        arr.append(float(y[2]))
+        arr.append(float(y[3]))
+        arr.extend(build_single_feature_row(X))
+        arcep_mat.append(arr)
+    nump = np.asarray(arcep_mat)
+    np.save('timitTrain',nump)
+    return
+
+build_timit_data()

LPC_Estimate_Class.py

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+from __future__ import print_function, division
+import torch
+import torch.nn as nn
+from torch.autograd import Variable
+import torch.nn.functional as F
+from torch import optim
+import numpy as np
+
+train_data = np.load("timitTrain.npy")
+test_data = np.load("timitTest.npy")
+Xtrain = train_data[:,5:].astype(np.float32)
+Ytrain = train_data[:,1:5].astype(np.float32)
+Xtest = test_data[:,5:].astype(np.float32)
+Ytest = test_data[:,1:5].astype(np.float32)
+
+use_cuda = torch.cuda.is_available()
+device = torch.device("cuda" if use_cuda else "cpu")
+_, D = Xtrain.shape
+K = len(Ytrain)
+
+print(D, K)
+
+class Net(nn.Module):
+
+    def __init__(self):
+        super(Net, self).__init__()
+        self.Dense1 = nn.Linear(D, 1024)
+        self.Dense2 = nn.Linear(1024, 512)
+        self.Dense3 = nn.Linear(512, 256)
+        self.out = nn.Linear(256, 4)
+
+    def forward(self, x):
+        x = torch.sigmoid(self.Dense1(x))
+        x = torch.sigmoid(self.Dense2(x))
+        x = torch.sigmoid(self.Dense3(x))
+        return self.out(x)
+
+
+
+
+loss = nn.L1Loss()
+
+def train(model, loss, optimizer, inputs, labels):
+    inputs = Variable(inputs.to(device))
+    labels = Variable(labels.to(device))
+    optimizer.zero_grad()
+
+    logits = model.forward(inputs)
+    output = loss.forward(logits, labels)
+    output.backward()
+    optimizer.step()
+
+    return output.item()
+
+
+def predict(model, inputs):
+    inputs = Variable(inputs)
+    logits = model.forward(inputs.to(device))
+    return logits.data.cpu().numpy()
+
+
+torch.manual_seed(0)
+
+Xtrain = torch.from_numpy(Xtrain).float().to(device)
+Ytrain = torch.from_numpy(Ytrain).float().to(device)
+Xtest = torch.from_numpy(Xtest).float().to(device)
+Ytest = torch.from_numpy(Ytest).float().to(device)
+
+model = Net().to(device)
+
+
+optimizer = optim.Adagrad(model.parameters(), lr=0.01)
+
+epochs = 80
+batchSize = 20
+n_batches = Xtrain.size()[0]
+
+costs = []
+test_accuracies = []
+print("Starting training ")
+for i in range(epochs):
+    cost = 0.0
+    for j in range(n_batches):
+        Xbatch = Xtrain[j*batchSize:(j+1)*batchSize]
+        Ybatch = Ytrain[j*batchSize:(j+1)*batchSize]
+        cost += train(model, loss, optimizer, Xbatch, Ybatch)
+
+    loss1 = 0.0
+    loss2 = 0.0
+    loss3 = 0.0
+    loss4 = 0.0
+    max_1 = 0.0
+    max_2 = 0.0
+    max_3 = 0.0
+    max_4 = 0.0
+    list_1 = []
+    list_2 = []
+    list_3 = []
+    list_4 = []
+    print('predicting...')
+    Ypred = predict(model, Xtest)
+    for k in range(0, len(Ytest)):
+        # print(y_hat[i])
+        l1 = np.abs(float(Ytest[k, 0]) - Ypred[k, 0])
+        l2 = np.abs(float(Ytest[k, 1]) - Ypred[k, 1])
+        l3 = np.abs(float(Ytest[k, 2]) - Ypred[k, 2])
+        l4 = np.abs(float(Ytest[k, 3]) - Ypred[k, 3])
+        list_1.append(l1)
+        list_2.append(l2)
+        list_3.append(l3)
+        list_4.append(l4)
+        max_1 = max(max_1, l1)
+        max_2 = max(max_2, l2)
+        max_3 = max(max_3, l3)
+        max_4 = max(max_4, l4)
+        loss1 += l1
+        loss2 += l2
+        loss3 += l3
+        loss4 += l4
+    loss1 /= len(Ytest)
+    loss2 /= len(Ytest)
+    loss3 /= len(Ytest)
+    loss4 /= len(Ytest)
+    total_loss = loss1 + loss2 + loss3 + loss4
+    total_loss /= 4.0
+    print('median: %.3f %.3f %.3f %.3f' % (np.median(list_1), np.median(list_2), np.median(list_3), np.median(list_4)))
+    print('max loss: %.3f %.3f %.3f %.3f' % (max_1, max_2, max_3, max_4))
+    print('Real test score: %.3f %.3f %.3f %.3f' % (loss1, loss2, loss3, loss4))
+    print("Epoch: %d, acc: %.3f" % (i, total_loss))
+
+    costs.append(cost/n_batches)
+    test_accuracies.append(round(total_loss, 3))
+    torch.save(model.state_dict(), "LPC_NN.pt")
+
+print(test_accuracies)

LPC_Model_inference.py

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+from __future__ import print_function, division
+import torch
+import torch.nn as nn
+from torch.autograd import Variable
+import torch.nn.functional as F
+from torch import optim
+import numpy as np
+
+test_data = np.load("timitTest.npy")
+Xtest = test_data[:,5:].astype(np.float32)
+Ytest = test_data[:,1:5].astype(np.float32)
+
+use_cuda = torch.cuda.is_available()
+device = torch.device("cuda" if use_cuda else "cpu")
+_, D = Xtest.shape
+print(D)
+
+class Net(nn.Module):
+
+    def __init__(self):
+        super(Net, self).__init__()
+        self.Dense1 = nn.Linear(D, 1024)
+        self.Dense2 = nn.Linear(1024, 512)
+        self.Dense3 = nn.Linear(512, 256)
+        self.out = nn.Linear(256, 4)
+
+    def forward(self, x):
+        x = torch.sigmoid(self.Dense1(x))
+        x = torch.sigmoid(self.Dense2(x))
+        x = torch.sigmoid(self.Dense3(x))
+        return self.out(x)
+
+def scaledLoss(output, target):
+	scale = torch.tensor([2.0, 1.0, .5, .1]).to(device)
+	loss = torch.abs(output - target)
+	scaled = loss*scale
+	return torch.mean(scaled)
+
+#loss = nn.L1Loss()
+
+def train(model, optimizer, inputs, labels):
+    inputs = Variable(inputs.to(device))
+    labels = Variable(labels.to(device))
+    optimizer.zero_grad()
+
+    logits = model.forward(inputs)
+    output = scaledLoss(logits, labels)
+    output.backward()
+    optimizer.step()
+
+    return output.item()
+
+
+def predict(model, inputs):
+    inputs = Variable(inputs)
+    logits = model.forward(inputs.to(device))
+    return logits.data.cpu().numpy()
+
+
+torch.manual_seed(0)
+
+Xtest = torch.from_numpy(Xtest).float().to(device)
+Ytest = torch.from_numpy(Ytest).float().to(device)
+
+model = Net().to(device)
+
+
+optimizer = optim.Adagrad(model.parameters(), lr=0.01)
+
+model.load_state_dict(torch.load("LPC_NN_scaledLoss.pt"))
+model.eval()
+loss1 = 0.0
+loss2 = 0.0
+loss3 = 0.0
+loss4 = 0.0
+max_1 = 0.0
+max_2 = 0.0
+max_3 = 0.0
+max_4 = 0.0
+list_1 = []
+list_2 = []
+list_3 = []
+list_4 = []
+print('predicting...')
+Ypred = predict(model, Xtest)
+for k in range(0, len(Ytest)):
+	# print(y_hat[i])
+	l1 = np.abs(float(Ytest[k, 0]) - Ypred[k, 0])
+	l2 = np.abs(float(Ytest[k, 1]) - Ypred[k, 1])
+	l3 = np.abs(float(Ytest[k, 2]) - Ypred[k, 2])
+	l4 = np.abs(float(Ytest[k, 3]) - Ypred[k, 3])
+	list_1.append(l1)
+	list_2.append(l2)
+	list_3.append(l3)
+	list_4.append(l4)
+	max_1 = max(max_1, l1)
+	max_2 = max(max_2, l2)
+	max_3 = max(max_3, l3)
+	max_4 = max(max_4, l4)
+	loss1 += l1
+	loss2 += l2
+	loss3 += l3
+	loss4 += l4
+loss1 /= len(Ytest)
+loss2 /= len(Ytest)
+loss3 /= len(Ytest)
+loss4 /= len(Ytest)
+total_loss = loss1 + loss2 + loss3 + loss4
+total_loss /= 4.0
+print('median: %.3f %.3f %.3f %.3f' % (np.median(list_1), np.median(list_2), np.median(list_3), np.median(list_4)))
+print('max loss: %.3f %.3f %.3f %.3f' % (max_1, max_2, max_3, max_4))
+print('Real test score: %.3f %.3f %.3f %.3f' % (loss1, loss2, loss3, loss4))
+print("acc: %.3f" % (total_loss))
+

Models/models

@@ -0,0 +1 @@
+

spectrogramEstimate.py

@@ -0,0 +1,141 @@
+from __future__ import print_function, division
+import torch
+import torch.nn as nn
+from torch.autograd import Variable
+import torch.nn.functional as F
+from torch import optim
+import numpy as np
+torch.manual_seed(1)
+
+trainY = np.load("norm_cnn_timit_train_Y.npy")
+testY = np.load("norm_cnn_timit_test_Y.npy")
+Xtrain = np.load("norm_cnn_timit_train_X.npy").astype(np.float32)
+Ytrain = trainY[:,1:5].astype(np.float32)
+Xtest = np.load("norm_cnn_timit_test_X.npy").astype(np.float32)
+Ytest = testY[:,1:5].astype(np.float32)
+
+use_cuda = torch.cuda.is_available()
+device = torch.device("cuda" if use_cuda else "cpu")
+D = Xtrain.shape[1]
+K = len(Ytrain)
+
+print(D, K)
+
+class Net(nn.Module):
+
+    def __init__(self):
+        super(Net, self).__init__()
+        self.Conv1 = nn.Conv2d(in_channels=1, out_channels=96, kernel_size=(3, 3), stride=1, padding=0)
+        self.Conv2 = nn.Conv2d(in_channels=96, out_channels=32, kernel_size=(3, 3), stride=1, padding=0)
+        self.Conv3 = nn.Conv2d(in_channels=32, out_channels=64, kernel_size=(3, 3), stride=1, padding=0)
+        self.Conv4 = nn.Conv2d(in_channels=64, out_channels=64, kernel_size=(5, 5), stride=1, padding=0)
+        self.Dense5 = nn.Linear(43*38*64, 512)
+        self.out = nn.Linear(512, 4)
+
+    def forward(self, x):
+        in_size = x.size(0)
+        x = F.relu(self.Conv1(x))
+        x = F.relu(self.Conv2(x))
+        x = F.max_pool2d(x, kernel_size=2, stride=1)
+        x = F.relu(self.Conv3(x))
+        x = F.relu(self.Conv4(x))
+        x = F.max_pool2d(x, kernel_size=2, stride=1)
+        #print(in_size)
+        x = x.view(x.size(0), -1)
+        x = F.relu(self.Dense5(x))
+        return self.out(x)
+
+
+def train(model, loss, optimizer, inputs, labels):
+    inputs = Variable(inputs.to(device))
+    labels = Variable(labels.to(device))
+    optimizer.zero_grad()
+
+    logits = model.forward(inputs)
+    output = loss.forward(logits, labels)
+    output.backward()
+    optimizer.step()
+
+    return output.item()
+
+
+def predict(model, inputs):
+    inputs = Variable(inputs)
+    with torch.no_grad():
+        logits = model.forward(inputs.to(device))
+    return logits.data.cpu().numpy()
+
+
+Xtrain = torch.from_numpy(Xtrain).float().to(device)
+Ytrain = torch.from_numpy(Ytrain).float().to(device)
+Xtest = torch.from_numpy(Xtest).float().to(device)
+Ytest = torch.from_numpy(Ytest).float().to(device)
+
+
+model = Net().to(device)
+loss = nn.L1Loss()
+optimizer = optim.Adagrad(model.parameters())
+
+epochs = 80
+batchSize = 32
+n_batches = int(np.floor(Xtrain.size()[0]/batchSize))
+
+costs = []
+test_accuracies = []
+print("Starting training ")
+for i in range(epochs):
+    cost = 0.0
+    for j in range(n_batches):
+        #print(j, '/', n_batches)
+        Xbatch = Xtrain[j*batchSize:(j+1)*batchSize]
+        Ybatch = Ytrain[j*batchSize:(j+1)*batchSize]
+        cost += train(model, loss, optimizer, Xbatch, Ybatch)
+
+    loss1 = 0.0
+    loss2 = 0.0
+    loss3 = 0.0
+    loss4 = 0.0
+    max_1 = 0.0
+    max_2 = 0.0
+    max_3 = 0.0
+    max_4 = 0.0
+    list_1 = []
+    list_2 = []
+    list_3 = []
+    list_4 = []
+    print('predicting...')
+    Ypred = predict(model, Xtest)
+    for k in range(0, len(Ytest)):
+        # print(y_hat[i])
+        l1 = np.abs(float(Ytest[k, 0]) - Ypred[k, 0])
+        l2 = np.abs(float(Ytest[k, 1]) - Ypred[k, 1])
+        l3 = np.abs(float(Ytest[k, 2]) - Ypred[k, 2])
+        l4 = np.abs(float(Ytest[k, 3]) - Ypred[k, 3])
+        list_1.append(l1)
+        list_2.append(l2)
+        list_3.append(l3)
+        list_4.append(l4)
+        max_1 = max(max_1, l1)
+        max_2 = max(max_2, l2)
+        max_3 = max(max_3, l3)
+        max_4 = max(max_4, l4)
+        loss1 += l1
+        loss2 += l2
+        loss3 += l3
+        loss4 += l4
+    loss1 /= len(Ytest)
+    loss2 /= len(Ytest)
+    loss3 /= len(Ytest)
+    loss4 /= len(Ytest)
+    total_loss = loss1 + loss2 + loss3 + loss4
+    total_loss /= 4.0
+    print('median: %.3f %.3f %.3f %.3f' % (np.median(list_1), np.median(list_2), np.median(list_3), np.median(list_4)))
+    print('max loss: %.3f %.3f %.3f %.3f' % (max_1, max_2, max_3, max_4))
+    print('Real test score: %.3f %.3f %.3f %.3f' % (loss1, loss2, loss3, loss4))
+    print("Epoch: %d, acc: %.3f" % (i, total_loss))
+
+    costs.append(cost/n_batches)
+    test_accuracies.append(round(total_loss, 3))
+    torch.save(model.state_dict(), "CNN_estimate.pt")
+
+print(test_accuracies)

spectrogramEstimate_inference.py

@@ -0,0 +1,121 @@
+from __future__ import print_function, division
+import torch
+import torch.nn as nn
+from torch.autograd import Variable
+import torch.nn.functional as F
+from torch import optim
+import numpy as np
+torch.manual_seed(1)
+
+testY = np.load("norm_cnn_timit_test_Y.npy")
+Xtest = np.load("norm_cnn_timit_test_X.npy").astype(np.float32)
+Ytest = testY[:,1:5].astype(np.float32)
+
+use_cuda = torch.cuda.is_available()
+device = torch.device("cuda" if use_cuda else "cpu")
+D = Xtest.shape
+print(D)
+
+print(Xtest.shape[1], len(Ytest))
+
+ 
+class Net(nn.Module):
+
+    def __init__(self):
+        super(Net, self).__init__()
+        self.Conv1 = nn.Conv2d(in_channels=1, out_channels=96, kernel_size=(3, 3), stride=1, padding=0)
+        self.Conv2 = nn.Conv2d(in_channels=96, out_channels=32, kernel_size=(3, 3), stride=1, padding=0)
+        self.Conv3 = nn.Conv2d(in_channels=32, out_channels=64, kernel_size=(3, 3), stride=1, padding=0)
+        self.Conv4 = nn.Conv2d(in_channels=64, out_channels=64, kernel_size=(5, 5), stride=1, padding=0)
+        self.Dense5 = nn.Linear(43*38*64, 512)
+        self.out = nn.Linear(512, 4)
+
+    def forward(self, x):
+        in_size = x.size(0)
+        x = F.relu(self.Conv1(x))
+        x = F.relu(self.Conv2(x))
+        x = F.max_pool2d(x, kernel_size=2, stride=1)
+        x = F.relu(self.Conv3(x))
+        x = F.relu(self.Conv4(x))
+        x = F.max_pool2d(x, kernel_size=2, stride=1)
+        #print(in_size)
+        x = x.view(x.size(0), -1)
+        x = F.relu(self.Dense5(x))
+        return self.out(x)
+
+
+def train(model, loss, optimizer, inputs, labels):
+    inputs = Variable(inputs.to(device))
+    labels = Variable(labels.to(device))
+    optimizer.zero_grad()
+
+    logits = model.forward(inputs)
+    output = loss.forward(logits, labels)
+    output.backward()
+    optimizer.step()
+
+    return output.item()
+
+
+def predict(model, inputs):
+    inputs = Variable(inputs)
+    with torch.no_grad():
+        logits = model.forward(inputs.to(device))
+    return logits.data.cpu().numpy()
+
+Xtest = torch.from_numpy(Xtest).float().to(device)
+Ytest = torch.from_numpy(Ytest).float().to(device)
+
+
+model = Net().to(device)
+loss = nn.L1Loss()
+optimizer = optim.Adagrad(model.parameters())
+
+model.load_state_dict(torch.load("CNN_estimate.pt"))
+model.eval()
+loss1 = 0.0
+loss2 = 0.0
+loss3 = 0.0
+loss4 = 0.0
+max_1 = 0.0
+max_2 = 0.0
+max_3 = 0.0
+max_4 = 0.0
+list_1 = []
+list_2 = []
+list_3 = []
+list_4 = []
+print('predicting...')
+Ypred1 = predict(model, Xtest[:1000])
+Ypred2 = predict(model, Xtest[1000:2000])
+Ypred3 = predict(model, Xtest[2000:])
+Ypred = np.concatenate((Ypred1, Ypred2, Ypred3))
+for k in range(0, len(Ytest)):
+	# print(y_hat[i])
+	l1 = np.abs(float(Ytest[k, 0]) - Ypred[k, 0])
+	l2 = np.abs(float(Ytest[k, 1]) - Ypred[k, 1])
+	l3 = np.abs(float(Ytest[k, 2]) - Ypred[k, 2])
+	l4 = np.abs(float(Ytest[k, 3]) - Ypred[k, 3])
+	list_1.append(l1)
+	list_2.append(l2)
+	list_3.append(l3)
+	list_4.append(l4)
+	max_1 = max(max_1, l1)
+	max_2 = max(max_2, l2)
+	max_3 = max(max_3, l3)
+	max_4 = max(max_4, l4)
+	loss1 += l1
+	loss2 += l2
+	loss3 += l3
+	loss4 += l4
+loss1 /= len(Ytest)
+loss2 /= len(Ytest)
+loss3 /= len(Ytest)
+loss4 /= len(Ytest)
+total_loss = loss1 + loss2 + loss3 + loss4
+total_loss /= 4.0
+print('median: %.3f %.3f %.3f %.3f' % (np.median(list_1), np.median(list_2), np.median(list_3), np.median(list_4)))
+print('max loss: %.3f %.3f %.3f %.3f' % (max_1, max_2, max_3, max_4))
+print('Real test score: %.3f %.3f %.3f %.3f' % (loss1, loss2, loss3, loss4))
+print("acc: %.3f" % (total_loss))
+