Course: Deep Learning for Solving and Estimating Dynamic Models in Economics and Finance
Script reference: §1.4–1.8 (Deep feedforward networks, backpropagation, initialization, regularization)
Notebook role: core
Author: Simon Scheidegger
RUN_MODE = "smoke" # one of: "smoke", "teaching", "production"
SEED = 0
A very basic example -- approximate functions with Deep Neural Networks and Tensorflow and Keras¶
This notebook contains two gentle examples how to perform supervised (regression and classification) machine learning tasks with fully connected deep neural networks.
we train a deep NN to learn an analytical, 2-dimensional function and perform regression.
we look at a standart data set (Zalando fashion MNIST dataset) to perform classification. This data-set is already avaialble within the Keras API.
From the technical side
we look at different, pre-implemented cost functions (e.g., MSE, MAE, Cross-entropy loss).
we look at initialization of the network weights.
early stopping.
we look at dropout.
we look at batch normalization.
A comprehensive set of examples can be found here and here:
The basic setup¶
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
print(tf.__version__)
import numpy as np
import math
import random
from random import uniform
import matplotlib.pyplot as plt
# Reproducibility: fix seeds across numpy / Python random / TensorFlow.
# Each notebook re-run reproduces the same plots.
SEED = 0
np.random.seed(SEED)
random.seed(SEED)
tf.random.set_seed(SEED)
2026-04-23 21:45:20.487024: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:477] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
E0000 00:00:1776973520.505373 31466 cuda_dnn.cc:8310] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1776973520.510892 31466 cuda_blas.cc:1418] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
2026-04-23 21:45:20.529661: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
2.18.0
1. A simple regression example¶
As a first example, we want to approximate 2-d analytical functions on a d-dimensional unit cube [-1,1]^d.
Generate training data¶
dim_x = 2 #input dimension of the analytical function
dim_y = 1 #output dimension of the analytical function
no_samples = 10000 #number of observations
no_test = 1000 #test data
filename = 'my_fun' #where to store data
#Generate training data
#x coord
aPnts = np.empty([no_samples, dim_x])
for iI in range(no_samples):
for iJ in range(dim_x):
aPnts[iI][iJ] = uniform(-1.0, 1.0)
data = aPnts #np.random.random((no_samples, dim_x))
#y value
aTres = np.empty([no_samples,])
for iI in range(no_samples):
aTres[iI] = math.cos(0.5 * math.pi * aPnts[iI][0]) * math.cos(0.5 * math.pi * aPnts[iI][1])
labels = aTres #np.random.random((no_samples,dim_y ))
#Test data
aPnts2 = np.empty([no_test, dim_x])
for iI in range(no_test):
for iJ in range(dim_x):
aPnts2[iI][iJ] = uniform(-1.0, 1.0)
data2 = aPnts2 #np.random.random((no_samples, dim_x))
## y value
aTres2 = np.empty([no_test,])
for iI in range(no_test):
aTres2[iI] = math.cos(0.5 * math.pi * aPnts2[iI][0]) * math.cos(0.5 * math.pi * aPnts2[iI][1])
labels2 = aTres2 #np.random.random((no_samples,dim_y ))
Fully-connected deep neural network -- creating a Sequential model¶
Tensorflow with the Keras API defines a Sequential model to be the appropriate choice for a plain stack of layers, where each layer has exactly one input tensor and one output tensor.
Schematically, the following Sequential model:
#Define Sequential model with 3 layers
model = tf.keras.Sequential([
#Adds a densely-connected layer with 64 units to the model:
layers.Dense(64, activation='relu', input_shape=(dim_x,), name='layer1'),
# Add another:
layers.Dense(64, activation='relu', name='layer2'),
# Add an output layer with dim_y output units:
layers.Dense(dim_y)],name='my_first_model')/usr/local/lib/python3.10/dist-packages/keras/src/layers/core/dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.
super().__init__(activity_regularizer=activity_regularizer, **kwargs)
2026-04-23 21:45:23.607991: E external/local_xla/xla/stream_executor/cuda/cuda_driver.cc:152] failed call to cuInit: INTERNAL: CUDA error: Failed call to cuInit: UNKNOWN ERROR (303)
Also note that the Sequential constructor accepts a
nameargument, just like any layer or model in Keras. This is useful to annotate TensorBoard graphs with semantically meaningful names.Once a model is “built”, you can call its
summary()method to display its contents:
model.summary()Loading...
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Its layers are accessible via the layers attribute:
model.layers[<Dense name=layer1, built=True>,
<Dense name=layer2, built=True>,
<Dense name=dense, built=True>]What to do once you have a model¶
Once your model architecture is ready, you will want to:
Train your model, evaluate it, and run inference. See guide to training & evaluation with the built-in loops.
Save your model to disk and restore it. See guide to serialization & saving.
Speed up model training by leveraging multiple GPUs. See guide to multi-GPU and distributed training.
Compile the model¶
Before the model is ready for training, it needs a few more settings. These are added during the model’s compile step:
Loss function —This measures how accurate the model is during training. You want to minimize this function to “steer” the model in the right direction.
Optimizer —This is how the model is updated based on the data it sees and its loss function.
Metrics —Used to monitor the training and testing steps.
The first example below uses MSE and MAE.
The second example below for instance uses accuracy, the fraction of the images that are correctly classified.
#Configure a model for mean-squared error regression.
model.compile(optimizer=tf.keras.optimizers.Adam(0.01),
loss='mse', # mean squared error
metrics=['mae']) # mean absolute error
#fit model
model.fit(data, labels, epochs=3, batch_size=32)
#model.weights
#print("Number of weights after calling the model:", len(model.weights)) Epoch 1/3
1/313 ━━━━━━━━━━━━━━━━━━━━ 3:32 680ms/step - loss: 0.2692 - mae: 0.4528 44/313 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 0.1103 - mae: 0.2576 90/313 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 0.0721 - mae: 0.1840136/313 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 0.0547 - mae: 0.1458180/313 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 0.0450 - mae: 0.1231226/313 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 0.0382 - mae: 0.1067271/313 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 0.0334 - mae: 0.0949313/313 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 0.0300 - mae: 0.0864313/313 ━━━━━━━━━━━━━━━━━━━━ 1s 1ms/step - loss: 0.0299 - mae: 0.0862
Epoch 2/3
1/313 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - loss: 1.9209e-04 - mae: 0.0126 45/313 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 9.2978e-05 - mae: 0.0077 90/313 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 8.1371e-05 - mae: 0.0071136/313 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 7.2474e-05 - mae: 0.0067185/313 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 6.7008e-05 - mae: 0.0064232/313 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 6.6634e-05 - mae: 0.0063280/313 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 6.7451e-05 - mae: 0.0064313/313 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 7.0795e-05 - mae: 0.0065
Epoch 3/3
1/313 ━━━━━━━━━━━━━━━━━━━━ 5s 17ms/step - loss: 6.9838e-04 - mae: 0.0200 48/313 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 7.1636e-04 - mae: 0.0212 94/313 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 7.0904e-04 - mae: 0.0211141/313 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 6.7925e-04 - mae: 0.0207184/313 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 6.3587e-04 - mae: 0.0198226/313 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 5.9342e-04 - mae: 0.0189268/313 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 5.5464e-04 - mae: 0.0180309/313 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 5.2165e-04 - mae: 0.0173313/313 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 5.1799e-04 - mae: 0.0172
<keras.src.callbacks.history.History at 0x7f5e1d31e050>Alternatively, you can also create a Sequential model incrementally via the
add()method:model = keras.Sequential()model.add(layers.Dense(2, activation="relu"))model.add(layers.Dense(3, activation="relu"))model.add(layers.Dense(4))Also note that the Sequential constructor accepts a
nameargument, just like any layer or model in Keras. This is useful to annotate TensorBoard graphs (later today) with semantically meaningful names.
Test Accuracy¶
test_loss, test_acc = model.evaluate(data2, labels2, verbose=2)
print('Test accuracy:', test_acc)32/32 - 0s - 5ms/step - loss: 1.7955e-04 - mae: 0.0112
Test accuracy: 0.011249595321714878
Predict individual values¶
predictions = model.predict(data2)
x = data2[0]
x1 = x[0,]
y1 = x[1,]
print('point to test', x[0,], ' ',x[1,])
#x.shape
#print(x.shape, data2.shape)
# Analytical solution:
res = math.cos(0.5 * math.pi * x1) * math.cos(0.5 * math.pi * y1)
print('NN prediction: ' , predictions[0], ', Analytical solution', res, ', Difference' ,abs(predictions[0]-res)) 1/32 ━━━━━━━━━━━━━━━━━━━━ 1s 40ms/step32/32 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step
point to test -0.3984225377298929 -0.2168186308086606
NN prediction: [0.753192] , Analytical solution 0.763918958611239 , Difference [0.01072693]
Save weights to a TensorFlow Checkpoint file -> Restart etc.¶
# Save weights to a TensorFlow Checkpoint file.
# Newer Keras versions require the filename to end in ".weights.h5".
ckpt_path = filename + ".weights.h5"
print("store the model ->", ckpt_path)
model.save_weights(ckpt_path)
# this requires a model with the same architecture.
print("reload the model")
model.load_weights(ckpt_path)
predictions = model.predict(data2)
res = math.cos(0.5 * math.pi * x1) * math.cos(0.5 * math.pi * y1)
print('NN prediction: ' , predictions[0], ', Analytical solution', res, ', Difference' ,abs(predictions[0]-res))
store the model -> my_fun.weights.h5
reload the model
1/32 ━━━━━━━━━━━━━━━━━━━━ 0s 14ms/step32/32 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step
NN prediction: [0.753192] , Analytical solution 0.763918958611239 , Difference [0.01072693]
Network weight initialization and early stopping¶
#Define Sequential model
#initialize weigths
initializer = tf.keras.initializers.RandomNormal(mean=0., stddev=1.)
#create the model
model2 = tf.keras.Sequential([
#Adds a densely-connected layer with 64 units to the model:
layers.Dense(64, activation='relu', input_shape=(dim_x,),kernel_initializer=initializer, name='layer1'),
# Add another:
layers.Dense(64, activation='relu', kernel_initializer=initializer, name='layer2'),
# Add an output layer with dim_y output units:
layers.Dense(dim_y)],name='my_first_model')Assuming the goal of a training is to minimize the loss. With this, the metric to be monitored would be ‘loss’, and mode would be ‘min’. A model.fit() training loop will check at end of every epoch whether the loss is no longer decreasing, considering the min_delta and patience if applicable. Once it’s found no longer decreasing, model.stop_training is marked True and the training terminates.
The quantity to be monitored needs to be available in logs dict. To make it so, pass the loss or metrics at model.compile().
Note: below, we use patience: one typically defines a patience, i.e., the number of epochs to wait before early stop if no progress on the validation set. In practice, the patience is often set somewhere between 10 and 20, but it really depends on your dataset and network.
# This callback will stop the training when there is no improvement in
# the validation loss for three consecutive epochs.
callback = tf.keras.callbacks.EarlyStopping(monitor='loss', patience=3)
#Configure a model for mean-squared error regression.
model2.compile(optimizer=tf.keras.optimizers.Adam(0.01),
loss='mse', # mean squared error
metrics=['mae']) # mean absolute error
#fit model
history = model2.fit(data, labels, epochs=3, batch_size=32,callbacks=[callback], verbose=0)
#early stopping
len(history.history['loss']) # Only 4 epochs are run.3This callback will stop the training when there is no improvement in the quantity to be monitored
the validation loss for delta -- which is the inimum change in the monitored quantity to qualify as an improvement, i.e. an absolute change of less than
min_delta, will count as no improvement.
callback = tf.keras.callbacks.EarlyStopping(monitor='loss', min_delta=0.01)
#Configure a model for mean-squared error regression.
model2.compile(optimizer=tf.keras.optimizers.Adam(0.01),
loss='mse', # mean squared error
metrics=['mae']) # mean absolute error
#fit model
history = model2.fit(data, labels, epochs=3, batch_size=32,callbacks=[callback], verbose=1)
len(history.history['loss']) # Only 2 epochs are run.Epoch 1/3
1/313 ━━━━━━━━━━━━━━━━━━━━ 3:21 646ms/step - loss: 0.0014 - mae: 0.0272 42/313 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 0.5419 - mae: 0.5184 80/313 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 0.3767 - mae: 0.3984123/313 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 0.2856 - mae: 0.3200169/313 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 0.2298 - mae: 0.2682212/313 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 0.1958 - mae: 0.2353257/313 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 0.1704 - mae: 0.2101301/313 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 0.1517 - mae: 0.1914313/313 ━━━━━━━━━━━━━━━━━━━━ 1s 1ms/step - loss: 0.1471 - mae: 0.1867
Epoch 2/3
1/313 ━━━━━━━━━━━━━━━━━━━━ 5s 17ms/step - loss: 0.0014 - mae: 0.0319 45/313 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 0.0022 - mae: 0.0362 92/313 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 0.0019 - mae: 0.0340139/313 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 0.0018 - mae: 0.0325181/313 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 0.0017 - mae: 0.0318222/313 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 0.0017 - mae: 0.0314266/313 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 0.0016 - mae: 0.0312307/313 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 0.0017 - mae: 0.0313313/313 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 0.0017 - mae: 0.0313
Epoch 3/3
1/313 ━━━━━━━━━━━━━━━━━━━━ 5s 17ms/step - loss: 6.9530e-04 - mae: 0.0202 42/313 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 9.5664e-04 - mae: 0.0244 87/313 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 9.6671e-04 - mae: 0.0243130/313 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 0.0010 - mae: 0.0248 173/313 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 0.0011 - mae: 0.0257216/313 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 0.0012 - mae: 0.0265259/313 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 0.0014 - mae: 0.0279303/313 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 0.0016 - mae: 0.0294313/313 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 0.0017 - mae: 0.0298
3Dropout layers¶
Dropout can be applied to input neurons called the visible layer.
In the example below we add a new Dropout layer between the input (or visible layer) and the first hidden layer. The dropout rate is set to 20%.
#Define Sequential model
model3 = tf.keras.Sequential([
#Adds a densely-connected layer with 64 units to the model:
layers.Dense(64, activation='relu', input_shape=(dim_x,), name='layer1'),
# Add another:
layers.Dense(64, activation='relu', name='layer2'),
#Dropout
layers.Dropout(0.2),
# Add another:
layers.Dense(64, activation='relu', name='layer3'),
#Dropout
layers.Dropout(0.2),
# Add an output layer with dim_y output units:
layers.Dense(dim_y)],name='my_first_model')
#Configure a model for mean-squared error regression.
model3.compile(optimizer=tf.keras.optimizers.Adam(0.01),
loss='mse', # mean squared error
metrics=['mae']) # mean absolute error
#fit model
model3.fit(data, labels, epochs=3, batch_size=32, verbose=1) Epoch 1/3
1/313 ━━━━━━━━━━━━━━━━━━━━ 4:38 894ms/step - loss: 0.3038 - mae: 0.4947 36/313 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 0.1175 - mae: 0.2684 69/313 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 0.0827 - mae: 0.2122105/313 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 0.0646 - mae: 0.1797143/313 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 0.0534 - mae: 0.1585183/313 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 0.0458 - mae: 0.1434221/313 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 0.0406 - mae: 0.1328261/313 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 0.0365 - mae: 0.1242299/313 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 0.0335 - mae: 0.1178313/313 ━━━━━━━━━━━━━━━━━━━━ 1s 1ms/step - loss: 0.0324 - mae: 0.1156
Epoch 2/3
1/313 ━━━━━━━━━━━━━━━━━━━━ 6s 20ms/step - loss: 0.0030 - mae: 0.0427 37/313 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 0.0038 - mae: 0.0477 76/313 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 0.0039 - mae: 0.0482115/313 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 0.0039 - mae: 0.0482152/313 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 0.0038 - mae: 0.0479190/313 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 0.0038 - mae: 0.0477229/313 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 0.0038 - mae: 0.0476267/313 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 0.0038 - mae: 0.0475305/313 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 0.0037 - mae: 0.0474313/313 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 0.0037 - mae: 0.0473
Epoch 3/3
1/313 ━━━━━━━━━━━━━━━━━━━━ 5s 17ms/step - loss: 0.0059 - mae: 0.0558 42/313 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 0.0032 - mae: 0.0440 82/313 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 0.0030 - mae: 0.0430120/313 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 0.0030 - mae: 0.0426160/313 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 0.0030 - mae: 0.0425201/313 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 0.0030 - mae: 0.0423242/313 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 0.0029 - mae: 0.0422283/313 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 0.0029 - mae: 0.0420313/313 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 0.0029 - mae: 0.0419
<keras.src.callbacks.history.History at 0x7f5e1425d2a0>#Inspect the network
model3.summary()Loading...
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Tips for using Dropout¶
The original paper on Dropout provides experimental results on a suite of standard machine learning problems. As a result they provide a number of useful heuristics to consider when using dropout in practice.
Generally, use a small dropout value of 20%-50% of neurons with 20% providing a good starting point. A probability too low has minimal effect and a value too high results in under-learning by the network.
Use a larger network. You are likely to get better performance when dropout is used on a larger network, giving the model more of an opportunity to learn independent representations.
Use dropout on incoming (visible) as well as hidden units. Application of dropout at each layer of the network has shown good results.
A useful functionality¶
Note that there’s also a corresponding
pop()method to remove layers: a Sequential model behaves very much like a list of layers.
model3.pop()
print(len(model.layers)) # 23
Batch normalization¶
Normalize the activations of the previous layer at each batch, i.e. applies a transformation that maintains the mean activation close to 0 and the activation standard deviation close to 1.
#Define Sequential model with 3 layers
model4 = tf.keras.Sequential([
#Adds a densely-connected layer with 64 units to the model:
layers.Dense(64, activation='relu', input_shape=(dim_x,), name='layer1'),
# Add another:
layers.Dense(64, activation='relu', name='layer2'),
#Batch Normalization
layers.BatchNormalization(),
# Add another:
layers.Dense(64, activation='relu', name='layer3'),
#Batch Normalization
layers.BatchNormalization(),
# Add an output layer with dim_y output units:
layers.Dense(dim_y)],name='my_first_model')
#Configure a model for mean-squared error regression.
model4.compile(optimizer=tf.keras.optimizers.Adam(0.01),
loss='mse', # mean squared error
metrics=['mae']) # mean absolute error
#fit model
model4.fit(data, labels, epochs=3, batch_size=32, verbose=1)
Epoch 1/3
1/313 ━━━━━━━━━━━━━━━━━━━━ 7:51 2s/step - loss: 1.1552 - mae: 0.9151 31/313 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 1.0230 - mae: 0.6612 61/313 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.6643 - mae: 0.4874 89/313 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.5135 - mae: 0.4070121/313 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.4136 - mae: 0.3501150/313 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.3546 - mae: 0.3150182/313 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.3083 - mae: 0.2873215/313 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.2731 - mae: 0.2657249/313 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.2452 - mae: 0.2481282/313 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.2237 - mae: 0.2342313/313 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.2070 - mae: 0.2233313/313 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - loss: 0.2065 - mae: 0.2229
Epoch 2/3
1/313 ━━━━━━━━━━━━━━━━━━━━ 6s 19ms/step - loss: 0.0045 - mae: 0.0568 33/313 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.0082 - mae: 0.0728 62/313 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.0085 - mae: 0.0735 95/313 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.0085 - mae: 0.0732129/313 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.0084 - mae: 0.0726166/313 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.0084 - mae: 0.0725204/313 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 0.0086 - mae: 0.0731240/313 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 0.0087 - mae: 0.0734274/313 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 0.0087 - mae: 0.0735310/313 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - loss: 0.0087 - mae: 0.0734313/313 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.0087 - mae: 0.0734
Epoch 3/3
1/313 ━━━━━━━━━━━━━━━━━━━━ 5s 17ms/step - loss: 0.0029 - mae: 0.0411 33/313 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.0066 - mae: 0.0642 62/313 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.0073 - mae: 0.0669 94/313 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.0075 - mae: 0.0676127/313 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.0075 - mae: 0.0678160/313 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.0076 - mae: 0.0680172/313 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.0076 - mae: 0.0682191/313 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.0077 - mae: 0.0686220/313 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.0078 - mae: 0.0691250/313 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.0078 - mae: 0.0693282/313 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - loss: 0.0078 - mae: 0.0694313/313 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - loss: 0.0078 - mae: 0.0693
<keras.src.callbacks.history.History at 0x7f5e0067d8a0>2. Classification example¶
Getting the data set¶
fashion_mnist = keras.datasets.fashion_mnist
(train_images, train_labels), (test_images, test_labels) = fashion_mnist.load_data()Loading the dataset returns four NumPy arrays:
The train_images and train_labels arrays are the training set—the data the model uses to learn.
The model is tested against the test set, the test_images, and test_labels arrays.
The images are 28x28 NumPy arrays, with pixel values ranging from 0 to 255. The labels are an array of integers, ranging from 0 to 9. These correspond to the class of clothing the image represents:
class_names = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat',
'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']Explore the data¶
Let’s explore the format of the dataset before training the model. The following shows there are 60,000 images in the training set, with each image represented as 28 x 28 pixels:
#shape
train_images.shape(60000, 28, 28)#lables
len(train_labels)
train_labelsarray([9, 0, 0, ..., 3, 0, 5], dtype=uint8)# There are 10,000 images in the test set, and each image is represented as 28 x 28 pixels:
test_images.shape
(10000, 28, 28)The data must be preprocessed before training the network.
If you inspect the first image in the training set, you will see that the pixel values fall in the range of 0 to 255.
We need to scale these values to a range of 0 to 1 before feeding them to the neural network model. To do so, divide the values by 255. It’s important that the training set and the testing set be preprocessed in the same way.
plt.figure()
plt.imshow(train_images[0])
plt.colorbar()
plt.grid(False)
plt.show()
train_images = train_images / 255.0
test_images = test_images / 255.0Now, we visually verify that the data is in the correct format and that you’re ready to build and train the network, let’s display the first 25 images from the training set and display the class name below each image.
plt.figure(figsize=(10,10))
for i in range(16):
plt.subplot(4,4,i+1)
plt.xticks([])
plt.yticks([])
plt.grid(False)
plt.imshow(train_images[i], cmap=plt.cm.binary)
plt.xlabel(class_names[train_labels[i]])
plt.show()
Fully-connected deep neural network -- creating a Sequential model¶
#set-up the model
model = keras.Sequential([
keras.layers.Flatten(input_shape=(28, 28)),
keras.layers.Dense(128, activation='relu'),
keras.layers.Dense(10)
])
#compile the model
model.compile(optimizer='adam',
loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
metrics=['accuracy'])
#train the model
model.fit(train_images, train_labels, epochs=10)/usr/local/lib/python3.10/dist-packages/keras/src/layers/reshaping/flatten.py:37: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.
super().__init__(**kwargs)
Epoch 1/10
2026-04-23 21:45:36.705186: W external/local_xla/xla/tsl/framework/cpu_allocator_impl.cc:83] Allocation of 188160000 exceeds 10% of free system memory.
1/1875 ━━━━━━━━━━━━━━━━━━━━ 15:01 481ms/step - accuracy: 0.0938 - loss: 2.4835 15/1875 ━━━━━━━━━━━━━━━━━━━━ 6s 4ms/step - accuracy: 0.3011 - loss: 2.0206 31/1875 ━━━━━━━━━━━━━━━━━━━━ 6s 3ms/step - accuracy: 0.4057 - loss: 1.7521 47/1875 ━━━━━━━━━━━━━━━━━━━━ 6s 3ms/step - accuracy: 0.4618 - loss: 1.5861 61/1875 ━━━━━━━━━━━━━━━━━━━━ 6s 3ms/step - accuracy: 0.4956 - loss: 1.4844 75/1875 ━━━━━━━━━━━━━━━━━━━━ 6s 4ms/step - accuracy: 0.5234 - loss: 1.4034 96/1875 ━━━━━━━━━━━━━━━━━━━━ 5s 3ms/step - accuracy: 0.5554 - loss: 1.3129 121/1875 ━━━━━━━━━━━━━━━━━━━━ 5s 3ms/step - accuracy: 0.5836 - loss: 1.2328 143/1875 ━━━━━━━━━━━━━━━━━━━━ 5s 3ms/step - accuracy: 0.6029 - loss: 1.1775 166/1875 ━━━━━━━━━━━━━━━━━━━━ 4s 3ms/step - accuracy: 0.6189 - loss: 1.1307 189/1875 ━━━━━━━━━━━━━━━━━━━━ 4s 3ms/step - accuracy: 0.6316 - loss: 1.0928 211/1875 ━━━━━━━━━━━━━━━━━━━━ 4s 3ms/step - accuracy: 0.6417 - loss: 1.0627 235/1875 ━━━━━━━━━━━━━━━━━━━━ 4s 3ms/step - accuracy: 0.6511 - loss: 1.0343 259/1875 ━━━━━━━━━━━━━━━━━━━━ 4s 3ms/step - accuracy: 0.6596 - loss: 1.0091 283/1875 ━━━━━━━━━━━━━━━━━━━━ 4s 3ms/step - accuracy: 0.6670 - loss: 0.9869 307/1875 ━━━━━━━━━━━━━━━━━━━━ 3s 3ms/step - accuracy: 0.6736 - loss: 0.9670 329/1875 ━━━━━━━━━━━━━━━━━━━━ 3s 3ms/step - accuracy: 0.6790 - loss: 0.9505 351/1875 ━━━━━━━━━━━━━━━━━━━━ 3s 2ms/step - accuracy: 0.6840 - loss: 0.9355 375/1875 ━━━━━━━━━━━━━━━━━━━━ 3s 2ms/step - accuracy: 0.6890 - loss: 0.9202 398/1875 ━━━━━━━━━━━━━━━━━━━━ 3s 2ms/step - accuracy: 0.6935 - loss: 0.9068 422/1875 ━━━━━━━━━━━━━━━━━━━━ 3s 2ms/step - accuracy: 0.6978 - loss: 0.8937 446/1875 ━━━━━━━━━━━━━━━━━━━━ 3s 2ms/step - accuracy: 0.7018 - loss: 0.8816 472/1875 ━━━━━━━━━━━━━━━━━━━━ 3s 2ms/step - accuracy: 0.7059 - loss: 0.8695 495/1875 ━━━━━━━━━━━━━━━━━━━━ 3s 2ms/step - accuracy: 0.7092 - loss: 0.8594 521/1875 ━━━━━━━━━━━━━━━━━━━━ 3s 2ms/step - accuracy: 0.7126 - loss: 0.8488 547/1875 ━━━━━━━━━━━━━━━━━━━━ 3s 2ms/step - accuracy: 0.7158 - loss: 0.8388 574/1875 ━━━━━━━━━━━━━━━━━━━━ 3s 2ms/step - accuracy: 0.7189 - loss: 0.8291 599/1875 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - accuracy: 0.7216 - loss: 0.8206 623/1875 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - accuracy: 0.7241 - loss: 0.8128 649/1875 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - accuracy: 0.7267 - loss: 0.8048 676/1875 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - accuracy: 0.7292 - loss: 0.7969 704/1875 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - accuracy: 0.7317 - loss: 0.7892 731/1875 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - accuracy: 0.7340 - loss: 0.7822 758/1875 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - accuracy: 0.7362 - loss: 0.7755 784/1875 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - accuracy: 0.7381 - loss: 0.7693 809/1875 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - accuracy: 0.7399 - loss: 0.7636 835/1875 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - accuracy: 0.7417 - loss: 0.7580 863/1875 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - accuracy: 0.7435 - loss: 0.7522 891/1875 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - accuracy: 0.7453 - loss: 0.7467 918/1875 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - accuracy: 0.7469 - loss: 0.7417 945/1875 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - accuracy: 0.7484 - loss: 0.7368 972/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.7499 - loss: 0.73211002/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.7515 - loss: 0.72721029/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.7529 - loss: 0.72281056/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.7542 - loss: 0.71861084/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.7556 - loss: 0.71441113/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.7569 - loss: 0.71021139/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.7581 - loss: 0.70661166/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.7593 - loss: 0.70291193/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.7605 - loss: 0.69941221/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.7616 - loss: 0.69581249/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.7627 - loss: 0.69231276/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.7638 - loss: 0.68901303/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.7648 - loss: 0.68581329/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.7658 - loss: 0.68281355/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.7667 - loss: 0.67991381/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.7677 - loss: 0.67701408/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.7686 - loss: 0.67411436/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.7695 - loss: 0.67121464/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.7704 - loss: 0.66831490/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.7712 - loss: 0.66581519/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.7721 - loss: 0.66301547/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.7730 - loss: 0.66031575/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.7738 - loss: 0.65771601/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.7745 - loss: 0.65541625/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.7752 - loss: 0.65331652/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.7759 - loss: 0.65091676/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.7766 - loss: 0.64891703/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.7773 - loss: 0.64661729/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.7780 - loss: 0.64451757/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.7787 - loss: 0.64231783/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.7793 - loss: 0.64031811/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.7800 - loss: 0.63811840/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.7807 - loss: 0.63601867/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.7813 - loss: 0.63401875/1875 ━━━━━━━━━━━━━━━━━━━━ 4s 2ms/step - accuracy: 0.7815 - loss: 0.6333
Epoch 2/10
1/1875 ━━━━━━━━━━━━━━━━━━━━ 38s 20ms/step - accuracy: 0.8750 - loss: 0.5084 30/1875 ━━━━━━━━━━━━━━━━━━━━ 3s 2ms/step - accuracy: 0.8588 - loss: 0.4045 57/1875 ━━━━━━━━━━━━━━━━━━━━ 3s 2ms/step - accuracy: 0.8593 - loss: 0.3997 85/1875 ━━━━━━━━━━━━━━━━━━━━ 3s 2ms/step - accuracy: 0.8599 - loss: 0.3972 112/1875 ━━━━━━━━━━━━━━━━━━━━ 3s 2ms/step - accuracy: 0.8588 - loss: 0.3980 139/1875 ━━━━━━━━━━━━━━━━━━━━ 3s 2ms/step - accuracy: 0.8586 - loss: 0.3977 166/1875 ━━━━━━━━━━━━━━━━━━━━ 3s 2ms/step - accuracy: 0.8587 - loss: 0.3970 191/1875 ━━━━━━━━━━━━━━━━━━━━ 3s 2ms/step - accuracy: 0.8586 - loss: 0.3971 218/1875 ━━━━━━━━━━━━━━━━━━━━ 3s 2ms/step - accuracy: 0.8587 - loss: 0.3972 246/1875 ━━━━━━━━━━━━━━━━━━━━ 3s 2ms/step - accuracy: 0.8589 - loss: 0.3972 273/1875 ━━━━━━━━━━━━━━━━━━━━ 3s 2ms/step - accuracy: 0.8592 - loss: 0.3970 298/1875 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - accuracy: 0.8593 - loss: 0.3969 324/1875 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - accuracy: 0.8593 - loss: 0.3969 350/1875 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - accuracy: 0.8593 - loss: 0.3970 376/1875 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - accuracy: 0.8593 - loss: 0.3969 403/1875 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - accuracy: 0.8594 - loss: 0.3967 429/1875 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - accuracy: 0.8596 - loss: 0.3964 457/1875 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - accuracy: 0.8597 - loss: 0.3963 484/1875 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - accuracy: 0.8597 - loss: 0.3961 508/1875 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - accuracy: 0.8598 - loss: 0.3959 533/1875 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - accuracy: 0.8599 - loss: 0.3957 559/1875 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - accuracy: 0.8599 - loss: 0.3954 586/1875 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - accuracy: 0.8600 - loss: 0.3951 614/1875 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - accuracy: 0.8601 - loss: 0.3948 642/1875 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - accuracy: 0.8602 - loss: 0.3945 669/1875 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - accuracy: 0.8602 - loss: 0.3941 697/1875 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - accuracy: 0.8603 - loss: 0.3938 725/1875 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - accuracy: 0.8604 - loss: 0.3934 752/1875 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - accuracy: 0.8605 - loss: 0.3931 779/1875 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - accuracy: 0.8605 - loss: 0.3928 808/1875 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - accuracy: 0.8606 - loss: 0.3925 834/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.8606 - loss: 0.3923 861/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.8606 - loss: 0.3921 889/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.8607 - loss: 0.3919 918/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.8607 - loss: 0.3917 945/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.8607 - loss: 0.3915 971/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.8607 - loss: 0.3913 998/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.8608 - loss: 0.39121025/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.8608 - loss: 0.39101050/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.8609 - loss: 0.39081078/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.8609 - loss: 0.39061105/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.8610 - loss: 0.39041132/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.8610 - loss: 0.39021158/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.8611 - loss: 0.39001185/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.8611 - loss: 0.38981213/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.8612 - loss: 0.38961242/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.8612 - loss: 0.38941270/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.8613 - loss: 0.38921298/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.8613 - loss: 0.38901319/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.8614 - loss: 0.38881342/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.8614 - loss: 0.38861365/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.8615 - loss: 0.38841389/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.8615 - loss: 0.38821413/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.8616 - loss: 0.38801438/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.8616 - loss: 0.38781466/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.8617 - loss: 0.38761493/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.8617 - loss: 0.38741521/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.8617 - loss: 0.38721544/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.8618 - loss: 0.38711569/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.8618 - loss: 0.38691597/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.8619 - loss: 0.38671623/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.8619 - loss: 0.38651649/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.8620 - loss: 0.38631676/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.8620 - loss: 0.38611705/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.8621 - loss: 0.38591732/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.8621 - loss: 0.38571759/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.8622 - loss: 0.38551786/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.8622 - loss: 0.38531814/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.8623 - loss: 0.38511840/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.8623 - loss: 0.38491865/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.8624 - loss: 0.38471875/1875 ━━━━━━━━━━━━━━━━━━━━ 4s 2ms/step - accuracy: 0.8624 - loss: 0.3846
Epoch 3/10
1/1875 ━━━━━━━━━━━━━━━━━━━━ 46:18 1s/step - accuracy: 0.9062 - loss: 0.3331 11/1875 ━━━━━━━━━━━━━━━━━━━━ 9s 5ms/step - accuracy: 0.8700 - loss: 0.3307 34/1875 ━━━━━━━━━━━━━━━━━━━━ 5s 3ms/step - accuracy: 0.8710 - loss: 0.3315 62/1875 ━━━━━━━━━━━━━━━━━━━━ 4s 3ms/step - accuracy: 0.8728 - loss: 0.3357 88/1875 ━━━━━━━━━━━━━━━━━━━━ 4s 2ms/step - accuracy: 0.8737 - loss: 0.3357 114/1875 ━━━━━━━━━━━━━━━━━━━━ 3s 2ms/step - accuracy: 0.8732 - loss: 0.3371 142/1875 ━━━━━━━━━━━━━━━━━━━━ 3s 2ms/step - accuracy: 0.8735 - loss: 0.3375 170/1875 ━━━━━━━━━━━━━━━━━━━━ 3s 2ms/step - accuracy: 0.8737 - loss: 0.3379 197/1875 ━━━━━━━━━━━━━━━━━━━━ 3s 2ms/step - accuracy: 0.8735 - loss: 0.3387 222/1875 ━━━━━━━━━━━━━━━━━━━━ 3s 2ms/step - accuracy: 0.8734 - loss: 0.3394 247/1875 ━━━━━━━━━━━━━━━━━━━━ 3s 2ms/step - accuracy: 0.8734 - loss: 0.3399 273/1875 ━━━━━━━━━━━━━━━━━━━━ 3s 2ms/step - accuracy: 0.8735 - loss: 0.3401 301/1875 ━━━━━━━━━━━━━━━━━━━━ 3s 2ms/step - accuracy: 0.8735 - loss: 0.3405 329/1875 ━━━━━━━━━━━━━━━━━━━━ 3s 2ms/step - accuracy: 0.8735 - loss: 0.3411 357/1875 ━━━━━━━━━━━━━━━━━━━━ 3s 2ms/step - accuracy: 0.8735 - loss: 0.3416 385/1875 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - accuracy: 0.8735 - loss: 0.3419 413/1875 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - accuracy: 0.8736 - loss: 0.3421 437/1875 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - accuracy: 0.8736 - loss: 0.3423 465/1875 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - accuracy: 0.8736 - loss: 0.3425 492/1875 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - accuracy: 0.8736 - loss: 0.3427 518/1875 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - accuracy: 0.8737 - loss: 0.3428 545/1875 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - accuracy: 0.8737 - loss: 0.3429 574/1875 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - accuracy: 0.8738 - loss: 0.3429 600/1875 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - accuracy: 0.8738 - loss: 0.3429 625/1875 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - accuracy: 0.8739 - loss: 0.3429 650/1875 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - accuracy: 0.8739 - loss: 0.3428 677/1875 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - accuracy: 0.8740 - loss: 0.3428 704/1875 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - accuracy: 0.8740 - loss: 0.3427 730/1875 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - accuracy: 0.8741 - loss: 0.3426 756/1875 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - accuracy: 0.8742 - loss: 0.3425 782/1875 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - accuracy: 0.8742 - loss: 0.3425 809/1875 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - accuracy: 0.8743 - loss: 0.3424 835/1875 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - accuracy: 0.8743 - loss: 0.3424 861/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.8743 - loss: 0.3423 889/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.8744 - loss: 0.3423 917/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.8744 - loss: 0.3423 944/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.8744 - loss: 0.3423 968/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.8744 - loss: 0.3423 996/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.8745 - loss: 0.34231023/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.8745 - loss: 0.34221052/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.8746 - loss: 0.34221079/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.8747 - loss: 0.34211104/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.8747 - loss: 0.34201131/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.8748 - loss: 0.34201157/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.8748 - loss: 0.34191184/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.8749 - loss: 0.34191211/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.8749 - loss: 0.34181236/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.8749 - loss: 0.34181261/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.8750 - loss: 0.34171287/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.8750 - loss: 0.34161314/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.8751 - loss: 0.34151341/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.8751 - loss: 0.34151367/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.8752 - loss: 0.34141393/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.8752 - loss: 0.34131420/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.8752 - loss: 0.34121447/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.8753 - loss: 0.34111472/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.8753 - loss: 0.34101497/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.8753 - loss: 0.34101524/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.8753 - loss: 0.34091551/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.8754 - loss: 0.34081579/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.8754 - loss: 0.34071606/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.8755 - loss: 0.34071633/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.8755 - loss: 0.34061659/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.8755 - loss: 0.34051687/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.8756 - loss: 0.34041713/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.8756 - loss: 0.34031739/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.8757 - loss: 0.34021766/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.8757 - loss: 0.34011792/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.8757 - loss: 0.34001818/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.8758 - loss: 0.33991845/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.8758 - loss: 0.33981872/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.8759 - loss: 0.33971875/1875 ━━━━━━━━━━━━━━━━━━━━ 5s 2ms/step - accuracy: 0.8759 - loss: 0.3397
Epoch 4/10
1/1875 ━━━━━━━━━━━━━━━━━━━━ 37s 20ms/step - accuracy: 0.8750 - loss: 0.2457 29/1875 ━━━━━━━━━━━━━━━━━━━━ 3s 2ms/step - accuracy: 0.8744 - loss: 0.2931 57/1875 ━━━━━━━━━━━━━━━━━━━━ 3s 2ms/step - accuracy: 0.8786 - loss: 0.3046 85/1875 ━━━━━━━━━━━━━━━━━━━━ 3s 2ms/step - accuracy: 0.8807 - loss: 0.3069 109/1875 ━━━━━━━━━━━━━━━━━━━━ 3s 2ms/step - accuracy: 0.8816 - loss: 0.3082 136/1875 ━━━━━━━━━━━━━━━━━━━━ 3s 2ms/step - accuracy: 0.8830 - loss: 0.3081 163/1875 ━━━━━━━━━━━━━━━━━━━━ 3s 2ms/step - accuracy: 0.8838 - loss: 0.3080 190/1875 ━━━━━━━━━━━━━━━━━━━━ 3s 2ms/step - accuracy: 0.8842 - loss: 0.3086 217/1875 ━━━━━━━━━━━━━━━━━━━━ 3s 2ms/step - accuracy: 0.8846 - loss: 0.3092 245/1875 ━━━━━━━━━━━━━━━━━━━━ 3s 2ms/step - accuracy: 0.8849 - loss: 0.3098 273/1875 ━━━━━━━━━━━━━━━━━━━━ 3s 2ms/step - accuracy: 0.8852 - loss: 0.3101 300/1875 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - accuracy: 0.8853 - loss: 0.3106 327/1875 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - accuracy: 0.8854 - loss: 0.3112 352/1875 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - accuracy: 0.8854 - loss: 0.3118 379/1875 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - accuracy: 0.8854 - loss: 0.3123 405/1875 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - accuracy: 0.8854 - loss: 0.3126 432/1875 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - accuracy: 0.8854 - loss: 0.3130 458/1875 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - accuracy: 0.8854 - loss: 0.3134 485/1875 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - accuracy: 0.8854 - loss: 0.3138 510/1875 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - accuracy: 0.8854 - loss: 0.3141 538/1875 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - accuracy: 0.8854 - loss: 0.3143 566/1875 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - accuracy: 0.8854 - loss: 0.3145 592/1875 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - accuracy: 0.8855 - loss: 0.3146 619/1875 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - accuracy: 0.8855 - loss: 0.3147 645/1875 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - accuracy: 0.8855 - loss: 0.3148 672/1875 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - accuracy: 0.8855 - loss: 0.3149 700/1875 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - accuracy: 0.8855 - loss: 0.3150 713/1875 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - accuracy: 0.8856 - loss: 0.3150 735/1875 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - accuracy: 0.8856 - loss: 0.3150 758/1875 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - accuracy: 0.8856 - loss: 0.3150 781/1875 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - accuracy: 0.8857 - loss: 0.3150 805/1875 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - accuracy: 0.8857 - loss: 0.3150 832/1875 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - accuracy: 0.8857 - loss: 0.3151 858/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.8857 - loss: 0.3151 886/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.8857 - loss: 0.3152 912/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.8857 - loss: 0.3152 938/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.8857 - loss: 0.3153 965/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.8857 - loss: 0.3153 991/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.8857 - loss: 0.31541019/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.8857 - loss: 0.31541049/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.8857 - loss: 0.31541076/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.8858 - loss: 0.31531102/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.8858 - loss: 0.31531125/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.8858 - loss: 0.31531149/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.8858 - loss: 0.31531176/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.8858 - loss: 0.31531203/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.8859 - loss: 0.31531230/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.8859 - loss: 0.31531258/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.8859 - loss: 0.31521285/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.8859 - loss: 0.31521312/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.8859 - loss: 0.31521340/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.8859 - loss: 0.31511369/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.8860 - loss: 0.31501397/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.8860 - loss: 0.31501424/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.8860 - loss: 0.31491450/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.8860 - loss: 0.31491476/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.8860 - loss: 0.31481504/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.8860 - loss: 0.31481532/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.8860 - loss: 0.31481560/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.8860 - loss: 0.31471589/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.8860 - loss: 0.31461617/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.8861 - loss: 0.31461646/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.8861 - loss: 0.31451669/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.8861 - loss: 0.31441693/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.8861 - loss: 0.31441719/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.8861 - loss: 0.31431747/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.8861 - loss: 0.31421773/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.8862 - loss: 0.31421799/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.8862 - loss: 0.31411827/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.8862 - loss: 0.31401853/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.8862 - loss: 0.31401875/1875 ━━━━━━━━━━━━━━━━━━━━ 4s 2ms/step - accuracy: 0.8862 - loss: 0.3139
Epoch 5/10
1/1875 ━━━━━━━━━━━━━━━━━━━━ 38s 20ms/step - accuracy: 0.9062 - loss: 0.2209 29/1875 ━━━━━━━━━━━━━━━━━━━━ 3s 2ms/step - accuracy: 0.8908 - loss: 0.2696 56/1875 ━━━━━━━━━━━━━━━━━━━━ 3s 2ms/step - accuracy: 0.8910 - loss: 0.2797 83/1875 ━━━━━━━━━━━━━━━━━━━━ 3s 2ms/step - accuracy: 0.8918 - loss: 0.2818 111/1875 ━━━━━━━━━━━━━━━━━━━━ 3s 2ms/step - accuracy: 0.8917 - loss: 0.2831 137/1875 ━━━━━━━━━━━━━━━━━━━━ 3s 2ms/step - accuracy: 0.8921 - loss: 0.2834 164/1875 ━━━━━━━━━━━━━━━━━━━━ 3s 2ms/step - accuracy: 0.8923 - loss: 0.2836 191/1875 ━━━━━━━━━━━━━━━━━━━━ 3s 2ms/step - accuracy: 0.8923 - loss: 0.2843 217/1875 ━━━━━━━━━━━━━━━━━━━━ 3s 2ms/step - accuracy: 0.8923 - loss: 0.2850 245/1875 ━━━━━━━━━━━━━━━━━━━━ 3s 2ms/step - accuracy: 0.8924 - loss: 0.2857 273/1875 ━━━━━━━━━━━━━━━━━━━━ 3s 2ms/step - accuracy: 0.8926 - loss: 0.2862 297/1875 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - accuracy: 0.8927 - loss: 0.2866 322/1875 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - accuracy: 0.8927 - loss: 0.2873 348/1875 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - accuracy: 0.8927 - loss: 0.2880 374/1875 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - accuracy: 0.8926 - loss: 0.2885 401/1875 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - accuracy: 0.8926 - loss: 0.2890 427/1875 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - accuracy: 0.8926 - loss: 0.2895 454/1875 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - accuracy: 0.8926 - loss: 0.2899 481/1875 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - accuracy: 0.8925 - loss: 0.2904 508/1875 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - accuracy: 0.8925 - loss: 0.2908 534/1875 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - accuracy: 0.8925 - loss: 0.2911 562/1875 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - accuracy: 0.8925 - loss: 0.2913 585/1875 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - accuracy: 0.8925 - loss: 0.2915 611/1875 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - accuracy: 0.8925 - loss: 0.2917 638/1875 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - accuracy: 0.8925 - loss: 0.2918 666/1875 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - accuracy: 0.8925 - loss: 0.2920 692/1875 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - accuracy: 0.8925 - loss: 0.2921 720/1875 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - accuracy: 0.8925 - loss: 0.2922 747/1875 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - accuracy: 0.8925 - loss: 0.2923 775/1875 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - accuracy: 0.8925 - loss: 0.2923 802/1875 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - accuracy: 0.8925 - loss: 0.2924 827/1875 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - accuracy: 0.8925 - loss: 0.2925 854/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.8925 - loss: 0.2926 882/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.8925 - loss: 0.2926 909/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.8925 - loss: 0.2927 937/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.8925 - loss: 0.2928 964/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.8925 - loss: 0.2929 991/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.8925 - loss: 0.29301018/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.8925 - loss: 0.29301044/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.8925 - loss: 0.29301072/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.8926 - loss: 0.29301100/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.8926 - loss: 0.29301125/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.8926 - loss: 0.29301151/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.8926 - loss: 0.29301177/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.8926 - loss: 0.29311204/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.8926 - loss: 0.29311232/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.8926 - loss: 0.29311260/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.8926 - loss: 0.29311288/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.8926 - loss: 0.29311316/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.8926 - loss: 0.29311341/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.8927 - loss: 0.29311365/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.8927 - loss: 0.29311393/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.8927 - loss: 0.29311421/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.8927 - loss: 0.29301447/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.8927 - loss: 0.29301473/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.8927 - loss: 0.29301502/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.8927 - loss: 0.29301530/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.8927 - loss: 0.29301557/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.8927 - loss: 0.29301583/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.8928 - loss: 0.29301612/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.8928 - loss: 0.29291640/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.8928 - loss: 0.29291666/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.8928 - loss: 0.29281692/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.8928 - loss: 0.29281720/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.8929 - loss: 0.29271748/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.8929 - loss: 0.29271776/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.8929 - loss: 0.29271804/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.8929 - loss: 0.29261832/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.8930 - loss: 0.29261859/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.8930 - loss: 0.29251875/1875 ━━━━━━━━━━━━━━━━━━━━ 4s 2ms/step - accuracy: 0.8930 - loss: 0.2925
Epoch 6/10
1/1875 ━━━━━━━━━━━━━━━━━━━━ 39s 21ms/step - accuracy: 0.9375 - loss: 0.1865 26/1875 ━━━━━━━━━━━━━━━━━━━━ 3s 2ms/step - accuracy: 0.9143 - loss: 0.2485 50/1875 ━━━━━━━━━━━━━━━━━━━━ 3s 2ms/step - accuracy: 0.9086 - loss: 0.2621 70/1875 ━━━━━━━━━━━━━━━━━━━━ 4s 2ms/step - accuracy: 0.9071 - loss: 0.2653 89/1875 ━━━━━━━━━━━━━━━━━━━━ 4s 2ms/step - accuracy: 0.9063 - loss: 0.2661 112/1875 ━━━━━━━━━━━━━━━━━━━━ 4s 2ms/step - accuracy: 0.9052 - loss: 0.2668 136/1875 ━━━━━━━━━━━━━━━━━━━━ 3s 2ms/step - accuracy: 0.9046 - loss: 0.2668 160/1875 ━━━━━━━━━━━━━━━━━━━━ 3s 2ms/step - accuracy: 0.9040 - loss: 0.2668 180/1875 ━━━━━━━━━━━━━━━━━━━━ 3s 2ms/step - accuracy: 0.9035 - loss: 0.2673 202/1875 ━━━━━━━━━━━━━━━━━━━━ 3s 2ms/step - accuracy: 0.9031 - loss: 0.2679 224/1875 ━━━━━━━━━━━━━━━━━━━━ 3s 2ms/step - accuracy: 0.9026 - loss: 0.2684 248/1875 ━━━━━━━━━━━━━━━━━━━━ 3s 2ms/step - accuracy: 0.9023 - loss: 0.2691 273/1875 ━━━━━━━━━━━━━━━━━━━━ 3s 2ms/step - accuracy: 0.9021 - loss: 0.2695 299/1875 ━━━━━━━━━━━━━━━━━━━━ 3s 2ms/step - accuracy: 0.9018 - loss: 0.2701 323/1875 ━━━━━━━━━━━━━━━━━━━━ 3s 2ms/step - accuracy: 0.9015 - loss: 0.2708 348/1875 ━━━━━━━━━━━━━━━━━━━━ 3s 2ms/step - accuracy: 0.9012 - loss: 0.2715 376/1875 ━━━━━━━━━━━━━━━━━━━━ 3s 2ms/step - accuracy: 0.9009 - loss: 0.2721 402/1875 ━━━━━━━━━━━━━━━━━━━━ 3s 2ms/step - accuracy: 0.9007 - loss: 0.2726 427/1875 ━━━━━━━━━━━━━━━━━━━━ 3s 2ms/step - accuracy: 0.9005 - loss: 0.2730 450/1875 ━━━━━━━━━━━━━━━━━━━━ 3s 2ms/step - accuracy: 0.9003 - loss: 0.2735 474/1875 ━━━━━━━━━━━━━━━━━━━━ 3s 2ms/step - accuracy: 0.9001 - loss: 0.2739 500/1875 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - accuracy: 0.9000 - loss: 0.2743 525/1875 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - accuracy: 0.8999 - loss: 0.2746 548/1875 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - accuracy: 0.8998 - loss: 0.2749 572/1875 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - accuracy: 0.8997 - loss: 0.2751 598/1875 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - accuracy: 0.8996 - loss: 0.2753 621/1875 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - accuracy: 0.8995 - loss: 0.2755 643/1875 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - accuracy: 0.8994 - loss: 0.2757 668/1875 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - accuracy: 0.8994 - loss: 0.2759 691/1875 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - accuracy: 0.8993 - loss: 0.2760 715/1875 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - accuracy: 0.8993 - loss: 0.2762 738/1875 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - accuracy: 0.8992 - loss: 0.2763 762/1875 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - accuracy: 0.8992 - loss: 0.2764 787/1875 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - accuracy: 0.8991 - loss: 0.2765 804/1875 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - accuracy: 0.8991 - loss: 0.2766 822/1875 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - accuracy: 0.8990 - loss: 0.2767 841/1875 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - accuracy: 0.8990 - loss: 0.2768 859/1875 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - accuracy: 0.8989 - loss: 0.2769 878/1875 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - accuracy: 0.8989 - loss: 0.2769 894/1875 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - accuracy: 0.8988 - loss: 0.2770 913/1875 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - accuracy: 0.8988 - loss: 0.2771 934/1875 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - accuracy: 0.8987 - loss: 0.2772 959/1875 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - accuracy: 0.8987 - loss: 0.2773 984/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.8987 - loss: 0.27741010/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.8986 - loss: 0.27741033/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.8986 - loss: 0.27751054/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.8986 - loss: 0.27751076/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.8986 - loss: 0.27751097/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.8986 - loss: 0.27761121/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.8986 - loss: 0.27761144/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.8986 - loss: 0.27761166/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.8986 - loss: 0.27771190/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.8985 - loss: 0.27771215/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.8985 - loss: 0.27771238/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.8985 - loss: 0.27781263/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.8985 - loss: 0.27781289/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.8985 - loss: 0.27781312/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.8985 - loss: 0.27781335/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.8985 - loss: 0.27781358/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.8985 - loss: 0.27781383/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.8985 - loss: 0.27781407/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.8985 - loss: 0.27781432/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.8985 - loss: 0.27781453/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.8985 - loss: 0.27781471/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.8985 - loss: 0.27781490/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.8985 - loss: 0.27781512/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.8985 - loss: 0.27781535/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.8985 - loss: 0.27781560/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.8985 - loss: 0.27781585/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.8985 - loss: 0.27781612/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.8985 - loss: 0.27781638/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.8985 - loss: 0.27771665/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.8985 - loss: 0.27771693/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.8985 - loss: 0.27771722/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.8985 - loss: 0.27761750/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.8986 - loss: 0.27761761/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.8986 - loss: 0.27761771/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.8986 - loss: 0.27761784/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.8986 - loss: 0.27761807/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.8986 - loss: 0.27751830/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.8986 - loss: 0.27751849/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.8986 - loss: 0.27751868/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.8986 - loss: 0.27751875/1875 ━━━━━━━━━━━━━━━━━━━━ 4s 2ms/step - accuracy: 0.8986 - loss: 0.2774
Epoch 7/10
1/1875 ━━━━━━━━━━━━━━━━━━━━ 46s 25ms/step - accuracy: 0.9375 - loss: 0.1603 25/1875 ━━━━━━━━━━━━━━━━━━━━ 3s 2ms/step - accuracy: 0.9225 - loss: 0.2356 51/1875 ━━━━━━━━━━━━━━━━━━━━ 3s 2ms/step - accuracy: 0.9159 - loss: 0.2476 74/1875 ━━━━━━━━━━━━━━━━━━━━ 3s 2ms/step - accuracy: 0.9141 - loss: 0.2503 96/1875 ━━━━━━━━━━━━━━━━━━━━ 3s 2ms/step - accuracy: 0.9127 - loss: 0.2511 122/1875 ━━━━━━━━━━━━━━━━━━━━ 3s 2ms/step - accuracy: 0.9114 - loss: 0.2515 146/1875 ━━━━━━━━━━━━━━━━━━━━ 3s 2ms/step - accuracy: 0.9105 - loss: 0.2516 170/1875 ━━━━━━━━━━━━━━━━━━━━ 3s 2ms/step - accuracy: 0.9097 - loss: 0.2519 196/1875 ━━━━━━━━━━━━━━━━━━━━ 3s 2ms/step - accuracy: 0.9089 - loss: 0.2525 224/1875 ━━━━━━━━━━━━━━━━━━━━ 3s 2ms/step - accuracy: 0.9081 - loss: 0.2533 254/1875 ━━━━━━━━━━━━━━━━━━━━ 3s 2ms/step - accuracy: 0.9075 - loss: 0.2541 279/1875 ━━━━━━━━━━━━━━━━━━━━ 3s 2ms/step - accuracy: 0.9072 - loss: 0.2546 301/1875 ━━━━━━━━━━━━━━━━━━━━ 3s 2ms/step - accuracy: 0.9068 - loss: 0.2551 322/1875 ━━━━━━━━━━━━━━━━━━━━ 3s 2ms/step - accuracy: 0.9065 - loss: 0.2558 345/1875 ━━━━━━━━━━━━━━━━━━━━ 3s 2ms/step - accuracy: 0.9060 - loss: 0.2565 367/1875 ━━━━━━━━━━━━━━━━━━━━ 3s 2ms/step - accuracy: 0.9057 - loss: 0.2570 393/1875 ━━━━━━━━━━━━━━━━━━━━ 3s 2ms/step - accuracy: 0.9054 - loss: 0.2575 416/1875 ━━━━━━━━━━━━━━━━━━━━ 3s 2ms/step - accuracy: 0.9051 - loss: 0.2580 443/1875 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - accuracy: 0.9049 - loss: 0.2585 471/1875 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - accuracy: 0.9047 - loss: 0.2590 491/1875 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - accuracy: 0.9045 - loss: 0.2593 512/1875 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - accuracy: 0.9044 - loss: 0.2596 537/1875 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - accuracy: 0.9043 - loss: 0.2599 562/1875 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - accuracy: 0.9041 - loss: 0.2602 586/1875 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - accuracy: 0.9040 - loss: 0.2604 611/1875 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - accuracy: 0.9039 - loss: 0.2606 632/1875 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - accuracy: 0.9038 - loss: 0.2608 655/1875 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - accuracy: 0.9038 - loss: 0.2610 672/1875 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - accuracy: 0.9037 - loss: 0.2611 693/1875 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - accuracy: 0.9037 - loss: 0.2613 714/1875 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - accuracy: 0.9037 - loss: 0.2614 736/1875 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - accuracy: 0.9036 - loss: 0.2615 753/1875 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - accuracy: 0.9036 - loss: 0.2616 773/1875 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - accuracy: 0.9035 - loss: 0.2617 795/1875 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - accuracy: 0.9035 - loss: 0.2618 820/1875 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - accuracy: 0.9034 - loss: 0.2619 845/1875 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - accuracy: 0.9034 - loss: 0.2620 871/1875 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - accuracy: 0.9033 - loss: 0.2621 894/1875 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - accuracy: 0.9033 - loss: 0.2622 920/1875 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - accuracy: 0.9032 - loss: 0.2623 946/1875 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - accuracy: 0.9032 - loss: 0.2624 972/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.9032 - loss: 0.2625 991/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.9032 - loss: 0.26261011/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.9032 - loss: 0.26261033/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.9032 - loss: 0.26271053/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.9032 - loss: 0.26271074/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.9032 - loss: 0.26271095/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.9032 - loss: 0.26271119/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.9032 - loss: 0.26281143/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.9032 - loss: 0.26281164/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.9032 - loss: 0.26281187/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.9032 - loss: 0.26291210/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.9032 - loss: 0.26291233/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.9032 - loss: 0.26291254/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.9032 - loss: 0.26291277/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.9032 - loss: 0.26301300/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.9032 - loss: 0.26301320/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.9032 - loss: 0.26301340/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.9032 - loss: 0.26301362/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.9032 - loss: 0.26301386/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.9032 - loss: 0.26301410/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.9032 - loss: 0.26301433/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9032 - loss: 0.26301458/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9032 - loss: 0.26301480/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9032 - loss: 0.26301501/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9032 - loss: 0.26301524/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9032 - loss: 0.26301549/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9032 - loss: 0.26301574/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9033 - loss: 0.26301598/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9033 - loss: 0.26301621/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9033 - loss: 0.26301646/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9033 - loss: 0.26291671/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9033 - loss: 0.26291695/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9033 - loss: 0.26291717/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9033 - loss: 0.26291740/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9033 - loss: 0.26281764/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9034 - loss: 0.26281790/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9034 - loss: 0.26281814/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9034 - loss: 0.26281840/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9034 - loss: 0.26281866/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9034 - loss: 0.26271875/1875 ━━━━━━━━━━━━━━━━━━━━ 4s 2ms/step - accuracy: 0.9034 - loss: 0.2627
Epoch 8/10
1/1875 ━━━━━━━━━━━━━━━━━━━━ 32s 17ms/step - accuracy: 0.9688 - loss: 0.1391 26/1875 ━━━━━━━━━━━━━━━━━━━━ 3s 2ms/step - accuracy: 0.9280 - loss: 0.2187 50/1875 ━━━━━━━━━━━━━━━━━━━━ 3s 2ms/step - accuracy: 0.9207 - loss: 0.2322 75/1875 ━━━━━━━━━━━━━━━━━━━━ 3s 2ms/step - accuracy: 0.9183 - loss: 0.2367 99/1875 ━━━━━━━━━━━━━━━━━━━━ 3s 2ms/step - accuracy: 0.9163 - loss: 0.2387 124/1875 ━━━━━━━━━━━━━━━━━━━━ 3s 2ms/step - accuracy: 0.9149 - loss: 0.2393 151/1875 ━━━━━━━━━━━━━━━━━━━━ 3s 2ms/step - accuracy: 0.9137 - loss: 0.2397 177/1875 ━━━━━━━━━━━━━━━━━━━━ 3s 2ms/step - accuracy: 0.9128 - loss: 0.2402 202/1875 ━━━━━━━━━━━━━━━━━━━━ 3s 2ms/step - accuracy: 0.9122 - loss: 0.2410 228/1875 ━━━━━━━━━━━━━━━━━━━━ 3s 2ms/step - accuracy: 0.9116 - loss: 0.2417 250/1875 ━━━━━━━━━━━━━━━━━━━━ 3s 2ms/step - accuracy: 0.9112 - loss: 0.2421 262/1875 ━━━━━━━━━━━━━━━━━━━━ 3s 2ms/step - accuracy: 0.9111 - loss: 0.2424 279/1875 ━━━━━━━━━━━━━━━━━━━━ 3s 2ms/step - accuracy: 0.9109 - loss: 0.2427 296/1875 ━━━━━━━━━━━━━━━━━━━━ 3s 2ms/step - accuracy: 0.9107 - loss: 0.2431 314/1875 ━━━━━━━━━━━━━━━━━━━━ 3s 2ms/step - accuracy: 0.9104 - loss: 0.2436 333/1875 ━━━━━━━━━━━━━━━━━━━━ 3s 2ms/step - accuracy: 0.9101 - loss: 0.2441 352/1875 ━━━━━━━━━━━━━━━━━━━━ 3s 2ms/step - accuracy: 0.9098 - loss: 0.2446 372/1875 ━━━━━━━━━━━━━━━━━━━━ 3s 2ms/step - accuracy: 0.9096 - loss: 0.2451 393/1875 ━━━━━━━━━━━━━━━━━━━━ 3s 2ms/step - accuracy: 0.9093 - loss: 0.2455 414/1875 ━━━━━━━━━━━━━━━━━━━━ 3s 2ms/step - accuracy: 0.9092 - loss: 0.2459 435/1875 ━━━━━━━━━━━━━━━━━━━━ 3s 2ms/step - accuracy: 0.9090 - loss: 0.2463 455/1875 ━━━━━━━━━━━━━━━━━━━━ 3s 2ms/step - accuracy: 0.9089 - loss: 0.2467 474/1875 ━━━━━━━━━━━━━━━━━━━━ 3s 2ms/step - accuracy: 0.9087 - loss: 0.2470 493/1875 ━━━━━━━━━━━━━━━━━━━━ 3s 2ms/step - accuracy: 0.9086 - loss: 0.2473 512/1875 ━━━━━━━━━━━━━━━━━━━━ 3s 2ms/step - accuracy: 0.9085 - loss: 0.2476 532/1875 ━━━━━━━━━━━━━━━━━━━━ 3s 2ms/step - accuracy: 0.9084 - loss: 0.2478 552/1875 ━━━━━━━━━━━━━━━━━━━━ 3s 2ms/step - accuracy: 0.9083 - loss: 0.2480 575/1875 ━━━━━━━━━━━━━━━━━━━━ 3s 2ms/step - accuracy: 0.9081 - loss: 0.2483 597/1875 ━━━━━━━━━━━━━━━━━━━━ 3s 2ms/step - accuracy: 0.9081 - loss: 0.2485 618/1875 ━━━━━━━━━━━━━━━━━━━━ 3s 2ms/step - accuracy: 0.9080 - loss: 0.2488 640/1875 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - accuracy: 0.9079 - loss: 0.2490 661/1875 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - accuracy: 0.9078 - loss: 0.2492 682/1875 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - accuracy: 0.9078 - loss: 0.2493 703/1875 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - accuracy: 0.9077 - loss: 0.2495 725/1875 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - accuracy: 0.9076 - loss: 0.2497 746/1875 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - accuracy: 0.9076 - loss: 0.2498 769/1875 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - accuracy: 0.9075 - loss: 0.2500 788/1875 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - accuracy: 0.9075 - loss: 0.2501 810/1875 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - accuracy: 0.9074 - loss: 0.2502 833/1875 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - accuracy: 0.9074 - loss: 0.2503 862/1875 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - accuracy: 0.9073 - loss: 0.2504 890/1875 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - accuracy: 0.9072 - loss: 0.2505 913/1875 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - accuracy: 0.9072 - loss: 0.2506 937/1875 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - accuracy: 0.9071 - loss: 0.2508 963/1875 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - accuracy: 0.9071 - loss: 0.2508 990/1875 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - accuracy: 0.9070 - loss: 0.25091016/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.9070 - loss: 0.25101039/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.9070 - loss: 0.25111063/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.9070 - loss: 0.25111087/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.9069 - loss: 0.25111109/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.9069 - loss: 0.25121126/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.9069 - loss: 0.25121143/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.9069 - loss: 0.25121160/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.9069 - loss: 0.25131174/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.9069 - loss: 0.25131187/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.9069 - loss: 0.25131205/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.9068 - loss: 0.25141224/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.9068 - loss: 0.25141243/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.9068 - loss: 0.25141264/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.9068 - loss: 0.25151284/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.9068 - loss: 0.25151302/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.9068 - loss: 0.25151318/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.9068 - loss: 0.25151336/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.9068 - loss: 0.25151357/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.9068 - loss: 0.25151380/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.9068 - loss: 0.25151403/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.9068 - loss: 0.25151427/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.9068 - loss: 0.25151451/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.9068 - loss: 0.25161474/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9068 - loss: 0.25161496/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9068 - loss: 0.25161518/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9068 - loss: 0.25161541/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9068 - loss: 0.25161566/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9068 - loss: 0.25161590/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9068 - loss: 0.25161611/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9068 - loss: 0.25151635/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9068 - loss: 0.25151658/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9068 - loss: 0.25151681/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9068 - loss: 0.25151703/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9068 - loss: 0.25141725/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9069 - loss: 0.25141745/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9069 - loss: 0.25141767/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9069 - loss: 0.25141789/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9069 - loss: 0.25141813/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9069 - loss: 0.25131835/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9069 - loss: 0.25131859/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9069 - loss: 0.25131875/1875 ━━━━━━━━━━━━━━━━━━━━ 4s 2ms/step - accuracy: 0.9069 - loss: 0.2513
Epoch 9/10
1/1875 ━━━━━━━━━━━━━━━━━━━━ 34s 18ms/step - accuracy: 0.9688 - loss: 0.1046 24/1875 ━━━━━━━━━━━━━━━━━━━━ 4s 2ms/step - accuracy: 0.9316 - loss: 0.2043 45/1875 ━━━━━━━━━━━━━━━━━━━━ 4s 2ms/step - accuracy: 0.9233 - loss: 0.2190 65/1875 ━━━━━━━━━━━━━━━━━━━━ 4s 2ms/step - accuracy: 0.9215 - loss: 0.2229 88/1875 ━━━━━━━━━━━━━━━━━━━━ 4s 2ms/step - accuracy: 0.9208 - loss: 0.2243 112/1875 ━━━━━━━━━━━━━━━━━━━━ 4s 2ms/step - accuracy: 0.9202 - loss: 0.2252 135/1875 ━━━━━━━━━━━━━━━━━━━━ 3s 2ms/step - accuracy: 0.9196 - loss: 0.2256 156/1875 ━━━━━━━━━━━━━━━━━━━━ 3s 2ms/step - accuracy: 0.9192 - loss: 0.2259 181/1875 ━━━━━━━━━━━━━━━━━━━━ 3s 2ms/step - accuracy: 0.9186 - loss: 0.2268 206/1875 ━━━━━━━━━━━━━━━━━━━━ 3s 2ms/step - accuracy: 0.9180 - loss: 0.2279 230/1875 ━━━━━━━━━━━━━━━━━━━━ 3s 2ms/step - accuracy: 0.9175 - loss: 0.2289 253/1875 ━━━━━━━━━━━━━━━━━━━━ 3s 2ms/step - accuracy: 0.9170 - loss: 0.2297 278/1875 ━━━━━━━━━━━━━━━━━━━━ 3s 2ms/step - accuracy: 0.9167 - loss: 0.2304 298/1875 ━━━━━━━━━━━━━━━━━━━━ 3s 2ms/step - accuracy: 0.9164 - loss: 0.2310 323/1875 ━━━━━━━━━━━━━━━━━━━━ 3s 2ms/step - accuracy: 0.9160 - loss: 0.2318 346/1875 ━━━━━━━━━━━━━━━━━━━━ 3s 2ms/step - accuracy: 0.9157 - loss: 0.2325 371/1875 ━━━━━━━━━━━━━━━━━━━━ 3s 2ms/step - accuracy: 0.9153 - loss: 0.2331 393/1875 ━━━━━━━━━━━━━━━━━━━━ 3s 2ms/step - accuracy: 0.9151 - loss: 0.2336 416/1875 ━━━━━━━━━━━━━━━━━━━━ 3s 2ms/step - accuracy: 0.9149 - loss: 0.2341 439/1875 ━━━━━━━━━━━━━━━━━━━━ 3s 2ms/step - accuracy: 0.9147 - loss: 0.2345 463/1875 ━━━━━━━━━━━━━━━━━━━━ 3s 2ms/step - accuracy: 0.9145 - loss: 0.2350 485/1875 ━━━━━━━━━━━━━━━━━━━━ 3s 2ms/step - accuracy: 0.9143 - loss: 0.2354 506/1875 ━━━━━━━━━━━━━━━━━━━━ 3s 2ms/step - accuracy: 0.9141 - loss: 0.2357 527/1875 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - accuracy: 0.9140 - loss: 0.2360 548/1875 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - accuracy: 0.9139 - loss: 0.2362 572/1875 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - accuracy: 0.9138 - loss: 0.2365 594/1875 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - accuracy: 0.9136 - loss: 0.2368 616/1875 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - accuracy: 0.9135 - loss: 0.2370 639/1875 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - accuracy: 0.9134 - loss: 0.2372 662/1875 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - accuracy: 0.9133 - loss: 0.2374 686/1875 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - accuracy: 0.9133 - loss: 0.2376 709/1875 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - accuracy: 0.9132 - loss: 0.2378 732/1875 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - accuracy: 0.9131 - loss: 0.2380 754/1875 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - accuracy: 0.9131 - loss: 0.2381 776/1875 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - accuracy: 0.9130 - loss: 0.2383 800/1875 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - accuracy: 0.9129 - loss: 0.2384 822/1875 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - accuracy: 0.9129 - loss: 0.2385 840/1875 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - accuracy: 0.9128 - loss: 0.2386 857/1875 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - accuracy: 0.9128 - loss: 0.2387 876/1875 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - accuracy: 0.9127 - loss: 0.2387 895/1875 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - accuracy: 0.9127 - loss: 0.2388 913/1875 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - accuracy: 0.9126 - loss: 0.2389 930/1875 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - accuracy: 0.9126 - loss: 0.2390 950/1875 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - accuracy: 0.9126 - loss: 0.2391 970/1875 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - accuracy: 0.9125 - loss: 0.2392 989/1875 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - accuracy: 0.9125 - loss: 0.23931002/1875 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - accuracy: 0.9125 - loss: 0.23931009/1875 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - accuracy: 0.9125 - loss: 0.23931021/1875 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - accuracy: 0.9124 - loss: 0.23941044/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.9124 - loss: 0.23951068/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.9124 - loss: 0.23951094/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.9124 - loss: 0.23951115/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.9124 - loss: 0.23961138/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.9124 - loss: 0.23961161/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.9123 - loss: 0.23971185/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.9123 - loss: 0.23971210/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.9123 - loss: 0.23981239/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.9123 - loss: 0.23981264/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.9123 - loss: 0.23991290/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.9123 - loss: 0.23991316/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.9123 - loss: 0.23991338/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.9123 - loss: 0.23991361/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.9123 - loss: 0.23991385/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.9123 - loss: 0.23991411/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.9123 - loss: 0.23991432/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.9123 - loss: 0.23991453/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9123 - loss: 0.24001477/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9123 - loss: 0.24001500/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9123 - loss: 0.24001522/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9123 - loss: 0.24001543/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9123 - loss: 0.24001564/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9123 - loss: 0.24001584/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9123 - loss: 0.24001605/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9123 - loss: 0.24001627/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9123 - loss: 0.23991650/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9124 - loss: 0.23991673/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9124 - loss: 0.23991697/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9124 - loss: 0.23991720/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9124 - loss: 0.23981743/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9124 - loss: 0.23981766/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9124 - loss: 0.23981786/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9124 - loss: 0.23981809/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9124 - loss: 0.23981832/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9124 - loss: 0.23971854/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9124 - loss: 0.23971875/1875 ━━━━━━━━━━━━━━━━━━━━ 4s 2ms/step - accuracy: 0.9124 - loss: 0.2397
Epoch 10/10
1/1875 ━━━━━━━━━━━━━━━━━━━━ 33s 18ms/step - accuracy: 0.9688 - loss: 0.1091 27/1875 ━━━━━━━━━━━━━━━━━━━━ 3s 2ms/step - accuracy: 0.9299 - loss: 0.1930 51/1875 ━━━━━━━━━━━━━━━━━━━━ 3s 2ms/step - accuracy: 0.9253 - loss: 0.2050 74/1875 ━━━━━━━━━━━━━━━━━━━━ 3s 2ms/step - accuracy: 0.9238 - loss: 0.2092 100/1875 ━━━━━━━━━━━━━━━━━━━━ 3s 2ms/step - accuracy: 0.9224 - loss: 0.2116 124/1875 ━━━━━━━━━━━━━━━━━━━━ 3s 2ms/step - accuracy: 0.9218 - loss: 0.2123 149/1875 ━━━━━━━━━━━━━━━━━━━━ 3s 2ms/step - accuracy: 0.9211 - loss: 0.2129 173/1875 ━━━━━━━━━━━━━━━━━━━━ 3s 2ms/step - accuracy: 0.9206 - loss: 0.2135 197/1875 ━━━━━━━━━━━━━━━━━━━━ 3s 2ms/step - accuracy: 0.9200 - loss: 0.2145 221/1875 ━━━━━━━━━━━━━━━━━━━━ 3s 2ms/step - accuracy: 0.9195 - loss: 0.2154 246/1875 ━━━━━━━━━━━━━━━━━━━━ 3s 2ms/step - accuracy: 0.9191 - loss: 0.2163 272/1875 ━━━━━━━━━━━━━━━━━━━━ 3s 2ms/step - accuracy: 0.9187 - loss: 0.2170 296/1875 ━━━━━━━━━━━━━━━━━━━━ 3s 2ms/step - accuracy: 0.9184 - loss: 0.2177 321/1875 ━━━━━━━━━━━━━━━━━━━━ 3s 2ms/step - accuracy: 0.9179 - loss: 0.2185 346/1875 ━━━━━━━━━━━━━━━━━━━━ 3s 2ms/step - accuracy: 0.9175 - loss: 0.2193 369/1875 ━━━━━━━━━━━━━━━━━━━━ 3s 2ms/step - accuracy: 0.9172 - loss: 0.2199 392/1875 ━━━━━━━━━━━━━━━━━━━━ 3s 2ms/step - accuracy: 0.9170 - loss: 0.2204 417/1875 ━━━━━━━━━━━━━━━━━━━━ 3s 2ms/step - accuracy: 0.9168 - loss: 0.2209 442/1875 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - accuracy: 0.9166 - loss: 0.2214 466/1875 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - accuracy: 0.9164 - loss: 0.2219 492/1875 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - accuracy: 0.9162 - loss: 0.2224 518/1875 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - accuracy: 0.9161 - loss: 0.2228 542/1875 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - accuracy: 0.9160 - loss: 0.2231 567/1875 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - accuracy: 0.9158 - loss: 0.2234 593/1875 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - accuracy: 0.9157 - loss: 0.2238 619/1875 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - accuracy: 0.9156 - loss: 0.2241 641/1875 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - accuracy: 0.9155 - loss: 0.2243 665/1875 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - accuracy: 0.9154 - loss: 0.2246 689/1875 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - accuracy: 0.9154 - loss: 0.2248 713/1875 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - accuracy: 0.9153 - loss: 0.2250 738/1875 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - accuracy: 0.9153 - loss: 0.2253 762/1875 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - accuracy: 0.9152 - loss: 0.2254 787/1875 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - accuracy: 0.9152 - loss: 0.2256 812/1875 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - accuracy: 0.9151 - loss: 0.2258 837/1875 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - accuracy: 0.9151 - loss: 0.2259 863/1875 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - accuracy: 0.9150 - loss: 0.2261 889/1875 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - accuracy: 0.9150 - loss: 0.2262 915/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.9149 - loss: 0.2264 941/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.9149 - loss: 0.2266 968/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.9149 - loss: 0.2267 996/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.9148 - loss: 0.22681024/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.9148 - loss: 0.22691051/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.9149 - loss: 0.22701078/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.9149 - loss: 0.22711105/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.9149 - loss: 0.22721131/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.9149 - loss: 0.22721158/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.9149 - loss: 0.22731185/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.9149 - loss: 0.22741212/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.9149 - loss: 0.22751240/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.9149 - loss: 0.22761267/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.9149 - loss: 0.22761295/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.9149 - loss: 0.22771322/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.9149 - loss: 0.22771350/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.9149 - loss: 0.22781374/1875 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - accuracy: 0.9149 - loss: 0.22781399/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9149 - loss: 0.22781425/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9149 - loss: 0.22791452/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9149 - loss: 0.22791479/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9149 - loss: 0.22801505/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9149 - loss: 0.22801532/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9150 - loss: 0.22801558/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9150 - loss: 0.22811583/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9150 - loss: 0.22811608/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9150 - loss: 0.22811634/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9150 - loss: 0.22811658/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9150 - loss: 0.22811683/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9150 - loss: 0.22811708/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9151 - loss: 0.22811734/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9151 - loss: 0.22811760/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9151 - loss: 0.22811787/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9151 - loss: 0.22801814/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9151 - loss: 0.22801840/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9152 - loss: 0.22801868/1875 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step - accuracy: 0.9152 - loss: 0.22811875/1875 ━━━━━━━━━━━━━━━━━━━━ 4s 2ms/step - accuracy: 0.9152 - loss: 0.2281
<keras.src.callbacks.history.History at 0x7f5ddec6dd20>#Evaluate the accuracy
test_loss, test_acc = model.evaluate(test_images, test_labels, verbose=2)
print('\nTest accuracy:', test_acc)313/313 - 0s - 1ms/step - accuracy: 0.8748 - loss: 0.3627
Test accuracy: 0.8748000264167786
Make predictions¶
With the model trained, you can use it to make predictions about some images.
The model’s linear outputs, logits.
Attach a softmax layer to convert the logits to probabilities, which are easier to interpret.
probability_model = tf.keras.Sequential([model,
tf.keras.layers.Softmax()])predictions = probability_model.predict(test_images)
#Lets have a look at the first prediction
predictions[0] 1/313 ━━━━━━━━━━━━━━━━━━━━ 15s 51ms/step 58/313 ━━━━━━━━━━━━━━━━━━━━ 0s 884us/step116/313 ━━━━━━━━━━━━━━━━━━━━ 0s 877us/step187/313 ━━━━━━━━━━━━━━━━━━━━ 0s 812us/step261/313 ━━━━━━━━━━━━━━━━━━━━ 0s 775us/step313/313 ━━━━━━━━━━━━━━━━━━━━ 0s 833us/step313/313 ━━━━━━━━━━━━━━━━━━━━ 0s 879us/step
array([1.6228274e-08, 1.2248317e-12, 4.4335303e-11, 2.3993746e-13,
1.7861275e-09, 3.1874544e-04, 8.8995421e-08, 1.7016764e-03,
3.4539929e-09, 9.9797946e-01], dtype=float32)A prediction is an array of 10 numbers. They represent the model’s “confidence” that the image corresponds to each of the 10 different articles of clothing. You can see which label has the highest confidence value:
np.argmax(predictions[0])9The model is most confident that this image is an ankle boot, or
class_names[9].Examining the test label shows that this classification is correct:
Verify predictions¶
With the model trained, you can use it to make predictions about some images.
Let’s look at the 0th image, predictions, and prediction array. Correct prediction labels are blue and incorrect prediction labels are red.
The number gives the percentage (out of 100) for the predicted label.
def plot_image(i, predictions_array, true_label, img):
predictions_array, true_label, img = predictions_array, true_label[i], img[i]
plt.grid(False)
plt.xticks([])
plt.yticks([])
plt.imshow(img, cmap=plt.cm.binary)
predicted_label = np.argmax(predictions_array)
if predicted_label == true_label:
color = 'blue'
else:
color = 'red'
plt.xlabel("{} {:2.0f}% ({})".format(class_names[predicted_label],
100*np.max(predictions_array),
class_names[true_label]),
color=color)
def plot_value_array(i, predictions_array, true_label):
predictions_array, true_label = predictions_array, true_label[i]
plt.grid(False)
plt.xticks(range(10))
plt.yticks([])
thisplot = plt.bar(range(10), predictions_array, color="#777777")
plt.ylim([0, 1])
predicted_label = np.argmax(predictions_array)
thisplot[predicted_label].set_color('red')
thisplot[true_label].set_color('blue')i = 10
plt.figure(figsize=(6,3))
plt.subplot(1,2,1)
plot_image(i, predictions[i], test_labels, test_images)
plt.subplot(1,2,2)
plot_value_array(i, predictions[i], test_labels)
plt.show()
# Take an image from the test dataset.
img = test_images[1]
print(img.shape)(28, 28)
# Add the image to a batch where it's the only member.
img = (np.expand_dims(img,0))
print(img.shape)(1, 28, 28)
# predict the correct label for this image:
predictions_single = probability_model.predict(img)
print(predictions_single)1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 13ms/step1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 33ms/step
[[4.9136605e-05 1.2148209e-10 9.9174941e-01 3.3324274e-09 8.1404252e-03
8.3491429e-11 6.0981321e-05 4.5754992e-20 4.6250884e-10 1.6255309e-12]]
plot_value_array(1, predictions_single[0], test_labels)
_ = plt.xticks(range(10), class_names, rotation=45)
keras.Model.predict returns a list of lists—one list for each image in the batch of data. Grab the predictions for our (only) image in the batch:
np.argmax(predictions_single[0])2